Systems and Methods for Generating Motion Forecast Data for a Plurality of Actors with Respect to an Autonomous Vehicle

ABSTRACT

A computing system can input first relative location embedding data into an interaction transformer model and receive, as an output of the interaction transformer model, motion forecast data for actors relative to a vehicle. The computing system can input the motion forecast data into a prediction model to receive respective trajectories for the actors for a current time step and respective projected trajectories for the actors for a subsequent time step. The computing system can generate second relative location embedding data based on the respective projected trajectories from the second time step. The computing system can produce second motion forecast data using the interaction transformer model based on the second relative location embedding. The computing system can determine second respective trajectories for the actors using the prediction model based on the second forecast data.

RELATED APPLICATION

The present application is based on and claims benefit of U.S. Provisional Patent Application No. 63/022,806 having a filing date of May 11, 2020, and U.S. Provisional Patent Application No. 62/936,438 having a filing date of Nov. 16, 2019, both of which are incorporated by reference herein.

FIELD

The present disclosure relates generally to controlling vehicles. In particular, the present disclosure is directed to systems and methods for generating motion forecast data for a plurality of actors with respect to an autonomous vehicle

BACKGROUND

Autonomous vehicles can be capable of sensing their environments and navigating with little to no human input. In particular, an autonomous vehicle can observe its surrounding environment using a variety of sensors and can attempt to comprehend the environment by performing various processing techniques on data collected by the sensors. Some vehicles can predict or project future circumstances based on current observations. However, the interactions between various third party actors can be complex and difficult to model.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or may be learned from the description, or may be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to a computing system. The computing system can include an interaction transformer model configured to receive a relative location embedding that describes relative respective locations of a plurality of actors with respect to an autonomous vehicle, and in response to receipt of the relative location embedding, generate motion forecast data with respect to the plurality of actors. The computing system can include a prediction model configured to receive the motion forecast data, and in response to receipt of the motion forecast data, generate respective trajectories for the plurality of actors for a current time step and respective projected trajectories for a subsequent time step. The computing system can include a memory that stores a set of instructions. The computing system can include one or more processors which use the set of instructions to, for a first iteration corresponding with a first time step, generate first motion forecast data for a plurality of actors at the first time step using the interaction transformer model based on a first relative location embedding. The set of instructions can be used, for a first iteration corresponding with a first time step, to input the first motion forecast data into the prediction model. The set of instructions can be used, for a first iteration corresponding with a first time step, to receive, as an output of the prediction model, first respective trajectories of the plurality of actors for the first time step and respective projected trajectories for a second time step that is after the first time step. The set of instructions can be used, for a second iteration corresponding with the second time step, to generate a second relative location embedding for the second time step based on the respective projected trajectories for the second time step from the first iteration. The set of instructions can be used, for a second iteration corresponding with the second time step, to analyze the second relative location embedding using the interaction transformer model to produce, as an output of the interaction transformer model, second motion forecast data for the plurality of actors at the second time step. The set of instructions can be used, for a second iteration corresponding with the second time step, to determine second respective trajectories of the plurality of actors for the second time step using the prediction model based on the second motion forecast data.

Another aspect of the present disclosure is directed to a computer-implemented method. The method can include, for a first iteration corresponding with a first time step, inputting a first relative location embedding that describes relative respective locations of a plurality of actors with respect to an autonomous vehicle into an interaction transformer model that is configured to receive a relative location embedding, and in response to receipt of the relative location embedding, generate motion forecast data with respect to the plurality of actors. The method can include, for a first iteration corresponding with a first time step, receiving, as an output of the interaction transformer model, first motion forecast data for a first plurality of actors at the first time step. The method can include, for a first iteration corresponding with a first time step, inputting the first motion forecast data into a prediction model, the prediction model configured to receive motion forecast data, and in response to receipt of the motion forecast data, generate respective trajectories for the plurality of actors for a current time step and respective projected trajectories for a subsequent time step. The method can include, for a first iteration corresponding with a first time step, receiving, as an output of the prediction model, first respective trajectories of the plurality of actors for the first time step and respective projected trajectories for a second time step that is after the first time step. The method can include, for a second iteration corresponding with the second time step, generating a second relative location embedding for the second time step based on the respective projected trajectories for the second time step from the first iteration. The method can include, for a second iteration corresponding with the second time step, analyzing, using the interaction transformer model, the second relative location embedding to produce, as an output of the interaction transformer model, second motion forecast data for the plurality of actors at the second time step. The method can include, for a second iteration corresponding with the second time step, determining, using the prediction model, second respective trajectories of the plurality of actors for the second time step using the prediction model based on the second motion forecast data.

Another aspect of the present disclosure is directed to a computer-implemented method for training one or more machine-learned models. The method can include, for a first iteration corresponding with a first time step, inputting a first relative location embedding that describes relative respective locations of a plurality of actors with respect to an autonomous vehicle into an interaction transformer model that is configured to receive a relative location embedding, and in response to receipt of the relative location embedding, generate motion forecast data with respect to the plurality of actors. The method can include, for a first iteration corresponding with a first time step, receiving, as an output of the interaction transformer model, first motion forecast data for a first plurality of actors at the first time step. The method can include, for a first iteration corresponding with a first time step, inputting the first motion forecast data into a prediction model, the prediction model configured to receive motion forecast data, and in response to receipt of the motion forecast data, generate respective trajectories for the plurality of actors for a current time step and respective projected trajectories for a subsequent time step. The method can include, for a first iteration corresponding with a first time step, receiving, as an output of the prediction model, first respective trajectories of the plurality of actors for the first time step and respective projected trajectories for a second time step that is after the first time step. The method can include, for a second iteration corresponding with the second time step, generating a second relative location embedding for the second time step based on the respective projected trajectories for the second time step from the first iteration. The method can include, for a second iteration corresponding with the second time step, analyzing, using the interaction transformer model, the second relative location embedding to produce, as an output of the interaction transformer model, second motion forecast data for the plurality of actors at the second time step. The method can include, for a second iteration corresponding with the second time step, determining, using the prediction model, second respective trajectories of the plurality of actors for the second time step using the prediction model based on the second motion forecast data. The method can include, for a second iteration corresponding with the second time step, adjusting one or more parameters of the interaction transformer model and the prediction model based on the second respective trajectories of the plurality of actors.

Other example aspects of the present disclosure are directed to systems, methods, vehicles, apparatuses, tangible, non-transitory computer-readable media, and memory devices for controlling autonomous vehicles.

The autonomous vehicle technology described herein can help improve the safety of passengers of an autonomous vehicle, improve the safety of the surroundings of the autonomous vehicle, improve the experience of the rider and/or operator of the autonomous vehicle, as well as provide other improvements as described herein. Moreover, the autonomous vehicle technology of the present disclosure can help improve the ability of an autonomous vehicle to effectively provide vehicle services to others and support the various members of the community in which the autonomous vehicle is operating, including persons with reduced mobility and/or persons that are underserved by other transportation options. Additionally, the autonomous vehicle of the present disclosure may reduce traffic congestion in communities as well as provide alternate forms of transportation that may provide environmental benefits.

These and other features, aspects and advantages of various embodiments will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the related principles.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill in the art are set forth in the specification, which makes reference to the appended figures, in which:

FIG. 1 depicts an example system overview according to example implementations of the present disclosure;

FIG. 2 depicts an example data flow diagram of an example prediction system according to example implementations of the present disclosure;

FIG. 3A depicts a simplified flow chart of an example implementation of a method for generating motion forecast data for a plurality of actors with respect to an autonomous vehicle;

FIG. 3B is a schematic illustration of the interaction transformer model of FIG. 3A;

FIG. 4 depicts an example flow diagram of an example method for iteratively generating respective trajectories for a plurality of actors with respect to an autonomous vehicle;

FIG. 5 depicts an example flow diagram of an example method for training a machine-learned interaction transformer model according to example implementations of the present disclosure;

FIG. 6 depicts example system components of an example system according to example implementations of the present disclosure; and

FIG. 7 depicts a block diagram of an example computing system according to example implementations of the present disclosure.

DETAILED DESCRIPTION

Reference now will be made in detail to embodiments, one or more example(s) of which are illustrated in the drawings. Each example is provided by way of explanation of the embodiments, not limitation of the present disclosure. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments without departing from the scope or spirit of the present disclosure. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that aspects of the present disclosure cover such modifications and variations.

Generally, the present disclosure is directed to systems and methods for generating motion forecast data for a plurality of actors with respect to an autonomous vehicle. Interaction between third party actors, such as vehicles, pedestrians, cyclists, and the like can alter how such third parties act. An actor can change its trajectory based on how it predicts another actor will act (e.g., its trajectory). For instance, when multiple vehicles approach a four-way stop, drivers anticipate how each will act to determine when to yield. Similarly, when one vehicle begins changing lanes, other drivers typically project a future trajectory of the vehicle. Other drivers can adjust their own trajectories based on this projection of the vehicles trajectory to prevent unsafe conditions, such as becoming dangerously close with the vehicle. Aspects of the present disclosure are directed to providing systems and method for autonomous vehicles that project third party trajectories of other actors based on anticipated interactions between the actors. Autonomous vehicles can greatly benefit from such systems to better navigate through and integrate into the modern driving environment (e.g., including human-driven vehicles and/or semi-autonomous vehicles).

More particularly, an autonomous vehicle can be a ground-based autonomous vehicle (e.g., car, truck, bus, bike, scooter, etc.) or another type of vehicle (e.g., aerial vehicle, etc.) that can operate with minimal and/or no interaction from a human operator. An autonomous vehicle can include a computing system (e.g., a vehicle computing system, etc.) located onboard the autonomous vehicle to help control the autonomous vehicle. The vehicle computing system can be located onboard the autonomous vehicle, in that the vehicle computing system can be located on or within the autonomous vehicle. The vehicle computing system can include one or more sensors, an autonomy computing system (e.g., for determining autonomous navigation), one or more vehicle control systems (e.g., for controlling braking, steering, powertrain, etc.), and/or other systems. The vehicle computing system can obtain sensor data from sensor(s) onboard the vehicle, attempt to comprehend the vehicle's surrounding environment by performing various processing techniques on the sensor data, and generate an appropriate motion plan through the vehicle's surrounding environment.

Aspects of the present disclosure are directed to using trajectories calculated in a prior iteration for a prior time step to generate current trajectories for the actors by generating the relative location embeddings based on predicted trajectories from the prior iteration and time step. This approach can be used to refine or smooth the trajectories generated for the actors. An interaction transformer model can be used to generate motion forecast data that describes trajectories and/or movement of a plurality of actors relative to the autonomous vehicle. More specifically, the interaction transformer model can generate the motion forecast data in response to receiving a relative location embedding that describes relative respective locations of a plurality of actors with respect to the autonomous vehicle. The vehicle computing system can input the motion forecast data from the interaction transformer model into a prediction model, and receive, as an output of the prediction model, respective trajectories for the plurality of actors for a first time step and respective projected trajectories for a second time step is that after the first time step. The vehicle computing system can, for a second iteration corresponding with a second time step, generate a relative location embedding for the second time step. The relative location embedding can be based on the respective projected trajectories from the first iteration, such that the predicted trajectories from the first iteration and first time step are incorporated in the determination of the trajectories for the second iteration and second time step. More particularly, the respective projected trajectories generated by the prediction system can be utilized as a relative location embedding for the interaction transformer model. Thus, at each time step predicted trajectories of the actors that were predicted in a prior time step can be considered (through the relative location embedding) when determining the current trajectories of the actors for the current time step.

The relative location embedding can then be used to produce motion forecast data that describes the current trajectories for the current time step. More particularly, the vehicle computing system can analyze this relative location embedding using the interaction transformer model to produce second motion forecast data for the plurality of actors at the second time step. The vehicle computing system can determine respective trajectories of the plurality of actors for the second time step, based on the second motion forecast data, using the prediction model. In such fashion, the vehicle computing system can iteratively generate outputs using the prediction model (e.g., projected trajectories) and provide this input to the interaction transformer. This process can refine or smooth the results of the interaction transformer, therefore leading to more accurate actor trajectory predictions.

The vehicle computing system can receive sensor data from one or more sensors that are coupled to or otherwise included within the autonomous vehicle. For example, in some implementations, a perception system can be included within the vehicle computing system and configured to receive the sensor data. As examples, the one or more sensors can include a Light Detection and Ranging (LIDAR) system, a Radio Detection and Ranging (RADAR) system, one or more cameras (e.g., visible spectrum cameras, infrared cameras, etc.), a positioning system (e.g., GPS), and/or other sensors. The sensor data can include information that describes the location of static objects and/or dynamic objects (actors) within the surrounding environment of the autonomous vehicle. For example, the objects can include traffic signals, additional vehicles, pedestrians, bicyclists, signs (e.g., stop signs, yield signs), and/or other objects. The sensor data can include raw sensor data and/or data that has been processed or manipulated in some manner before being provided to other systems within the autonomy computing system.

In addition to the sensor data, the vehicle computing system (e.g., the perception system) can retrieve or otherwise obtain map data that provides detailed information about the surrounding environment of the autonomous vehicle. The map data can provide information regarding: the identity and location of different roadways, road segments, buildings, or other items; the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway); traffic control data (e.g., the location, timing, and/or instructions of signage (e.g., stop signs, yield signs), traffic lights (e.g., stop lights), or other traffic signals or control devices/markings (e.g., cross walks)); and/or any other map data that provides information that assists the vehicle computing system in comprehending and perceiving its surrounding environment and its relationship thereto.

To help forecast data for objects/actors with respect to an autonomous vehicle, the sensor data (e.g., image frames, LIDAR sweeps, or other suitable sensor data) and/or map data can be processed before being input into a machine-learned model (e.g., an object detection model, interaction transformer model, etc). For example, a BEV voxel representation can be generated of LiDAR point clouds. For each sweep, the point cloud can be voxelized into a 3D occupancy grid, for example, with a fixed 3D voxel resolution, centered at the position of the autonomous vehicle (“ego car position”). The point feature can be defined as 1 to represent occupancy. The voxel feature can be computed by summing all nearby point features weighted by their relative positions to the voxel center. As a result, the voxel representation can implicitly preserve fine-grained information within the voxel. To capture past motion for the task of prediction, multiple past LiDAR sweeps can be aggregated. For example, the voxel representations of the LiDAR sweeps can be concatenating along the Z-axis. Past LiDAR sweeps can be registered to a current frame by “ego motion compensation.” Ego motion compensation generally refers to compensating for movement of the autonomous vehicle to with respect to sensor data. In this example, movement of the autonomous vehicle can be tracked using one or more tracking data sources (e.g., through image frame, accelerometers, or the like). The LiDAR data can be adjusted, reconciled, aligned, and/or mapped with respect to such tracking data to compensate for movement of the autonomous vehicle.

In some implementations, map data (e.g., geometric and/or semantic map priors) can be exploited for better reasoning. For example, ground height can be subtracted from the Z-value of each LiDAR point before voxelization. As a result, the variation caused by ground slope can be removed. Map data and/or semantic priors in the form of road and lane masks can be extracted. Each semantic prior can be or include a one-channel raster image in BEV depicting the drivable surface and all the lanes respectively. The LiDAR voxel representation can be augmented through concatenation with semantic map priors. The output space of the input representation can be defined in BEV, which can provide for efficient feature sharing between perception and prediction. Thus, the sensor and map data can be processed to generate an input representation that can be input the object detection model.

The object detection model can employ a sensor-fusion backbone network that can include a “two-stream” architecture. A “BEV stream network” can be or include a 2D convolutional network that extracts features in BEV space from the input representation (e.g., including joint LiDAR and map representations). Inception-like blocks can be stacked sequentially with residual connections to extract multi-scale feature maps. An “image stream network” can be or include residual blocks that are pre-trained. For instance, the image stream network can be or include a ResNet-18 model that is pre-trained on an ImageNet data set. The image stream network can be configured to receive camera images as input. Multi-scale image feature maps from each ResNet-18 residual block can be aggregated, for example using a feature pyramid network. The aggregated feature map can be fused with the BEV stream via a continuous fusion layer. Thus, the object detection model can employ a “two-stream” architecture to detect locations of actors with respect to the autonomous vehicle.

A “continuous fusion layer” can provide a dense fusion from image space to BEV space through linear interpolation parameterized by a trainable multi-layer perceptron (MLP). More specifically, image features can first be back-projected to BEV space according to the existing LiDAR observation. At BEV locations with no LiDAR points, the image features can be interpolated from nearby occupied locations (e.g., using one or more MLPs). The image feature and BEV feature can be fused together by element-wise addition (assuming same number of feature channels) in BEV space. Thus, the BEV stream of LiDAR data can be fused with camera images to generate the output feature map.

In some implementations, the output feature map can be input into a machine-learned object detection model, and object detection data can be received as an output of the machine-learned object detection model. The object detection data can include multi-sensor features for use in action prediction. For example, as vehicles do not overlap and have relatively similar size in BEV space, object detection can be formulated as dense prediction without introducing any object anchors. Several convolutions (e.g., 1×1) can be applied on top of the BEV feature map, which can output an n-dimensional vector per voxel. Non-maximum suppression (NMS) can be used to remove and/or reduce duplicates.

Aspects of the present disclosure are directed to “Recurrent Interactive Motion Forecasting,” which can refer to employing recurrent machine-learning models and/or techniques to predict the trajectories of other actors with respect to the autonomous vehicle. Recurrent interaction motion forecasting can provide various benefits. For example, a recurrent interaction motion forecasting can be configured to 1) jointly reason over all actors to capture their interactions and/or 2) iteratively infer each trajectory to capture the sequential nature of the trajectory. This design is informed by the following two observations. First, the behaviors of actors heavily depend on each other. For example, drivers control the vehicle speed to keep a safe distance from the vehicle ahead. At intersections, drivers typically wait for the other drivers that have the right of way. Second, the output trajectory can be considered to have a Markovian temporal dependency. That is, the output at each time step depends on the outputs at previous time steps. Thus, recurrent interaction motion forecasting, as described herein can provide various benefits.

In addition to the sensor data, the vehicle computing system (e.g., the perception system) can retrieve or otherwise obtain map data that provides detailed information about the surrounding environment of the autonomous vehicle. The map data can provide information regarding: the identity and location of different roadways, road segments, buildings, or other items; the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway); traffic control data (e.g., the location, timing, and/or instructions of signage (e.g., stop signs, yield signs), traffic lights (e.g., stop lights), or other traffic signals or control devices/markings (e.g., cross walks)); and/or any other map data that provides information that assists the vehicle computing system in comprehending and perceiving its surrounding environment and its relationship thereto.

Recurrent interaction motion forecasting, as described herein, can have an “interaction transformer” configuration. A “transformer” configuration generally refers to a sequence-to-sequence model with an encoder-decoder configuration or architecture. In some implementations, an input sequence can be generated that describes the object detection data. For example, the interaction model can generate the input sequence by projecting each feature of the object detection data to a query and a pair of keys and values, which can each be or include respective vectors. The input sequence can be input into an attention model, and attentional weights can be received as an output of the attention model. The interaction model can generate an attention embedding with respect to the plurality of actors in response to receipt of the attentional weights. In some implementations, a recurrent model can be configured to receive the attention embedding, and in response to receipt of the attention embedding, generate motion forecast data with respect to the plurality of actors.

More formally, the input sequence, F^(in) can be denoted as:

F ^(in)∈

where each row is a feature vector. The interaction model can use linear projections to get the set of queries, keys, and values as follows:

Q=F ^(in) W ^(Q) ,K=F ^(in) W ^(K) ,V=F ^(in) W ^(V)  (1)

where each of the following are matrices of weights:

W ^(Q)∈

W ^(K)∈

W ^(V)∈

The scaled dot products between the queries and keys can be used to compute the attentional weights. The values for each query can then be aggregated:

$\begin{matrix} {A = {{{softmax}\left( \frac{{QK}^{T}}{\sqrt{d_{k}}} \right)}V}} & (2) \end{matrix}$

where a softmax function can be used to add a sum-to-one normalization to the attentional weights of a query (each row of QK^(T)). The scaling factor

$\frac{1}{\sqrt{d_{k}}}$

can be used to prevent the dot product from being numerically too large. Finally, Transformer can use a set of non-linear transformations with shortcut connections to perform feature updates with the recurrent model to produce output features, F^(out):

F ^(out)=ResBlock(MLP(A)+F _(in))  (3)

where MLP denotes a Multi-Layer Perceptron which can be applied to each row of A; ResBlock denotes a residual block and can also be applied on each row, and F^(out) has the same shape as F^(in)

As indicated above, the input can correspond with a set of actors and their representations. The state of each actor can be represented with features extracted from the BEV feature map as well as the actor location, which can simply be, for example, the x and y coordinates of the actor center. Information about object size and orientation can be encoded in the feature map and thus may not be explicitly encoded in the actor representation.

To capture the sequential nature of the trajectory outputs, the recurrent interaction motion forecasting model can be used to predict the motion in an auto-regressive way. At each time step, an interaction transformer can be used to update the states of all actors and then predict the next waypoint of the trajectory for each. More specifically, an actor features matrix, F^(in), can be defined where each row is a feature vector of a respective actor. Equation (1), above, can be used to compute the queries. Instead of encoding location as absolute positional embeddings (which can be fused into the input features) relative location embedding can be employed:

R _(ij) =MLP(|x _(j) ^(i)|,|

|, sgn(x _(j) ^(i)), sgn(

),w _(j) ,l _(j),θ_(j) ^(i)),  (4)

where R∈

can be a 3-dimensional matrix and x_(j) ^(i), y_(j) ^(i), and θ_(j) ^(i) are the locations and orientation of actor j and transformed into actor i's local coordinate system, sgn can be the sign function, and w_(j) and l_(j) can encode the actor's size. A two-layer MLP can be used to transform the 7-channel input to the 16-channel embedding.

F^(in) and R can be used compute K and V. Both K and V can be 3-dimensional matrices:

K∈

V∈

K and V can be computed as follows:

K _(i) =MLP(Concat(F ^(in) W ^(K) ¹ ,R _(i) W ^(K) ² ))  (5)

V _(i)=Concat(F ^(nn) W ^(V) ¹ ,R _(i) W ^(V) ² )  (6)

In these two equations, Concat can denote concatenation along the second dimension, and MLP can be applied on each row vector. The attention embedding 358, A_(i), can be computed as follows:

$\begin{matrix} {A_{i} = {{{sigmoid}\left( \frac{Q_{i}K_{i}^{T}}{\sqrt{d_{k}}} \right)}V_{i}}} & (7) \end{matrix}$

Note the softmax function from Equation (2) can be changed to a sigmoid function. Further, removing the sum-to-one constraint can lead to better performance in this task.

As indicated, the object detection data can correspond with a set of actors and their representations. The state of each actor can be represented with features extracted from the BEV feature map as well as the actor location, which can simply be the x- and y-coordinates of the actor center. Information about object size and orientation can be encoded in the object detection data and thus does not have to be explicitly encoded in the actor representation.

In some implementations, relative distances and orientations can be employed as absolute location provides little information about the interactions between actors. At each time step, an interaction transformer model can update the states of all actors and then predict the next waypoint of the trajectory for each actor. Instead of encoding location as absolute positional embeddings (which can be fused into the input features), a relative location embedding can be employed with respect to the keys, values, and queries. The relative location embedding can, in some implementations, include the attention embedding and the object detection data with respect to the plurality of actors, as described previously. The attention embedding can be calculated based on relative location embedding, the keys, values, and queries.

More particularly, the vehicle computing system can obtain a relative location embedding (e.g., a location embedding with respect to the vehicle, etc.). The relative location embedding can describe relative respective locations of a plurality of actors (e.g., pedestrians, vehicles, etc.) with respect to the autonomous vehicle. An interaction transformer model (e.g., neural network(s), etc.) can be configured to receive the relative location embedding, and in response to receipt of the relative location embedding, generate motion forecast data with respect to the plurality of actors. As such, the vehicle computing system can, for a first iteration corresponding with a first time step, generate first motion forecast data for a plurality of actors at the first time step using the interaction transformer model based on a first relative location embedding.

In some implementations, the interaction transformer model can include an interaction model and a recurrent model (e.g., one or more neural network(s), neural network layer(s), etc.). The interaction model can be configured to receive the relative location embedding that describes the relative respective locations of the plurality of actors (e.g., vehicles, pedestrians, etc.) with respect to the autonomous vehicle. In response to receiving the relative location embedding, the interaction model can generate an attention embedding with respect to the plurality of actors. The attention embedding can be calculated based on the relative location embedding, the keys, values, and/or queries. More particularly, the attention embedding can, in some implementations, include a set of attentional weights using a compatibility function between the query and the set of keys of the relative location embedding. In some implementations, the recurrent model can be configured to receive the attention embedding. In response to receiving the attention embedding, the recurrent model can generate the motion forecast data with respect to the plurality of actors.

The vehicle computing system can input the first motion forecast data into a prediction model. The prediction model can be configured to receive motion forecast data, and in response to receipt of the motion forecast data, generate respective trajectories for a plurality of actors for a current time step and respective projected trajectories for a subsequent time step. As such, the vehicle computing system can input the first motion forecast data into the prediction model to receive, as an output of the prediction model, first respective trajectories for the plurality of actors for the first time step (e.g., a current trajectory or trajectory segment prediction for each of the actors, etc.) and respective projected trajectories for a second time step is that after the first time step (e.g., trajectory predictions corresponding to a time after the current trajectory predictions, etc.).

The vehicle computing system can, for a second iteration corresponding with the second time step, generate a second relative location embedding for the second time step. The second relative location embedding can be based on the respective projected trajectories for the second time step from the first iteration. The vehicle computing system can analyze the second relative location embedding using the interaction transformer model. By using the interaction transformer model, the vehicle computing system can produce, as an output of the interaction transformer model, second motion forecast data for the plurality of actors at the second time step. The vehicle computing system can determine respective trajectories of the plurality of actors for the second time step using the prediction model based on the second motion forecast data.

In addition to the sensor data, the vehicle computing system (e.g., the perception system) can retrieve or otherwise obtain map data that provides detailed information about the surrounding environment of the autonomous vehicle. The map data can provide information regarding: the identity and location of different roadways, road segments, buildings, or other items; the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway); traffic control data (e.g., the location, timing, and/or instructions of signage (e.g., stop signs, yield signs), traffic lights (e.g., stop lights), or other traffic signals or control devices/markings (e.g., cross walks)); and/or any other map data that provides information that assists the vehicle computing system in comprehending and perceiving its surrounding environment and its relationship thereto.

In some implementations, the interaction transformer module can be configured to generate the motion forecast data by receiving the relative location embedding, F, and an input sequence describing object detection data (e.g., an input sequence that projects features of the object detection data to a query and a pair of keys and values, etc.). In response to receiving both the relative location embedding and an input sequence describing object detection data, the interaction transformer module can generate the motion forecast data with respect to the plurality of actors. As such, in some implementations, the first motion forecast data can based on the first relative location embedding and a first input sequence corresponding with the first time step.

Further, in some implementations, the second motion forecast data can be produced by using the interaction transformer model to analyze the second relative location embedding and a second input sequence corresponding to the second time step. In some implementations, the second input sequence can be based at least in part on the first motion forecast data.

More formally, to capture the sequential nature of the trajectory outputs, at each time step, the interaction transformer model can update the states of all actors and then predict the next waypoint of the trajectory for each actor. The input actor features and relative location embedding at time step t can be denoted as F^(in(t)) (e.g., an input sequence describing object detection data, etc.) and R^((t)) (e.g., a relative location embedding, etc.). The detection bounding boxes can be used to compute R⁽⁰⁾, and set F^(in(0)) to be the bilinearly interpolated output BEV features extracted at the detection box centers. The interaction transformer model can take F^(in(t)) and R^((t)) and output F^(out(t)) (e.g., motion forecast data with respect to the plurality of actors, etc.), which can then be fed into a two-layer multi-layer perceptron (e.g., the prediction model, etc.) to get the prediction outputs.

The prediction outputs can include: (1) a refinement P_(refine) ^((t)) (e.g., respective trajectories for the plurality of actors for a current time step t, etc.), and (2) a proposal for t+1 denoted as P_(proposal) ^((t+1)) (e.g., respective projected trajectories for a subsequent time step, t+1, etc.). Both can be parameterized as (dx, dy, sin 2θ, cos 2θ, clsθ), where (dr, dy) can be relative to the detection box center at t=0, and (sin 2θ, cos 2θ, clsθ) can be defined in the same way as the detection output parameterizations. P_(refine) ^((t)) can be used as the final motion forecasting output for time step, t, (e.g., the motion forecast data for the current time step, etc.) and P_(proposal) ^((t+1)) can be used to compute R^((t+)) for the next step (e.g., the subsequent relative location embedding, etc.). F^(out(t)) can also be used as F^(in(t+1)) (e.g., the input sequence for the subsequent time step, etc.). The prediction model can be used to generate both the proposal and the refinement for simplicity.

In some implementations, the computing system can generate respective trajectory sequences for the plurality of actors. The respective trajectory sequences can include the first respective trajectories of the plurality of actors for the first time step and the second respective trajectories of the plurality of actors for the second time step. As an example, a first and second respective trajectory can be generated for a first actor. The first and second respective trajectories can respectively include a first and second segment of a complete route trajectory sequence (e.g., a complete navigation from location A to location B. In such fashion, the respective trajectories can be included (e.g., combined, etc.) sequentially as to seamlessly generate a full trajectory across a route.

In some implementations, the input sequences (e.g., the first input sequence, the second input sequence, etc.) can further describe one or more features for each of the plurality of actors. As an example, the second input sequence can describe these features at the first time step and/or the second time step. For example, a derivative of the location of the respective actor relative to the autonomous vehicle can be a feature described by the input sequence(s). For another example, a size of the respective actor, an orientation of the respective actor relative to the autonomous vehicle, and/or the center location of the respective actor can be feature(s) described by the input sequence(s).

In some implementations, the interaction transformer model and/or the prediction model can be or otherwise include one or more neural networks (e.g., recurrent neural networks, convolutional neural networks, feed-forward artificial neural networks, etc.). More particularly, in some implementations, the prediction model can be or otherwise include one or more multi-layer perceptrons.

Aspects of the present disclosure are directed to training one or more machine-learned models for generating motion forecast data for a plurality of actors with respect to an autonomous vehicle. The method can include evaluating a loss function that evaluates a difference between a ground truth associated with training data and the second respective trajectories for the plurality of actors and/or the respective projected trajectories for the second time step. In some implementations, the method can include adjusting one or more parameters of the interaction transformer model and/or the prediction model based on the evaluation of the loss function. As such, in some implementations, the two models can be trained in an “end-to-end configuration.” For example, errors can be sequentially back-propagated through each of the prediction model and the interaction transformer model to evaluate a joint loss function. A gradient of the joint loss function can be calculated to adjust the parameter(s) to reduce the joint loss function to jointly train the models.

More specifically, the model(s) can be fully differentiable and thus can be trainable end-to-end through back-propagation. The sum of a detection loss of the object detection model and a prediction loss of the interaction and/or recurrent models can be minimized. For example, an overall loss function can be a sum of respective losses for object classification, detection box regression, and future motion regression. In particular, binary cross entropy can be used as classification loss, and smooth L1 loss on each dimension of the detection box and its future motion. “L1” loss generally refers to mean absolute error.

In some implementations, hard negative mining can be employed. For object detection, the distance between BEV voxels and their closest ground-truth box centers can be used to determine positive and negative samples. Samples having distances smaller than a threshold can be considered as positive. Samples having distances larger than the threshold can be considered as negative. As a large proportion of the samples are negative in dense object detection, online hard negative mining can be employed. In some implementations, only the most difficult negative samples (with largest loss) can be kept and easy negative samples can be ignored. Classification loss can be averaged over both positive and negative samples while regression loss can be averaged over positive samples only.

In some implementations, online association can be performed between detection results and ground truth labels to compute prediction loss. For each detection, the ground-truth box can be assigned with the maximum (oriented) intersection of union “IoU.” If a ground truth box is assigned to multiple detections, only the detection with maximum “IoU” can be kept while other detections are ignored. Regression on future motion can then be averaged over those detections with the associated ground-truth.

Example aspects of the present disclosure can provide for a number of technical effects and benefits, including improvements to computing systems. For example, the computational time and resources required to accurately predict the trajectories of the third party actors can be reduced. Another example technical effect and benefit can include an improved safety assurance. In some cases, especially cases involving multiple actors and/or decisions, exhaustively testing every possibility can be computationally infeasible. Systems and methods according to the present disclosure can allow for an autonomous vehicle to safely navigate scenes having multiple objects and/or requiring multiple decisions that could otherwise be challenging or impossible to navigate effectively while considering the safety of each object and/or decision.

Various means can be configured to perform the methods and processes described herein. For example, a computing system can include sensor data obtaining unit(s), map data obtaining unit(s), machine-learned object recognition model application unit(s), trajectory/behavior forecasting unit(s), vehicle controlling unit(s), operator communication unit(s), data storing unit(s), and/or other means for performing the operations and functions described herein. In some implementations, one or more of the units may be implemented separately. In some implementations, one or more units may be a part of or included in one or more other units. These means can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s), dedicated circuit(s), application-specific integrated circuit(s), programmable array logic, field-programmable gate array(s), controller(s), microcontroller(s), and/or other suitable hardware. The means can also, or alternately, include software control means implemented with a processor or logic circuitry for example. The means can include or otherwise be able to access memory such as, for example, one or more non-transitory computer-readable storage media, such as random-access memory, read-only memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, flash/other memory device(s), data registrar(s), database(s), and/or other suitable hardware.

The means can be programmed to perform one or more algorithm(s) for carrying out the operations and functions described herein. For instance, the means can be configured to obtain sensor data from one or more sensors that generate sensor data relative to an autonomous vehicle. In some implementations, the means can be configured to obtain sensor data associated with the autonomous vehicle's surrounding environment as well as the position and movement of the autonomous vehicle. In some implementations, the means can be configured to obtain LIDAR data (e.g., a three-dimensional point cloud) obtained from a LIDAR system. In some implementations, the means can be configured to obtain image data obtained from one or more cameras. In some implementations, the means can be configured to obtain a birds-eye view representation of data obtained relative to the autonomous vehicle. In some implementations, the means can be configured to obtain sensor data represented as a multi-dimensional tensor having a height dimension and a time dimension stacked into a channel dimension associated with the multi-dimensional tensor. A sensor data obtaining unit is one example of a means for obtaining such sensor data as described herein.

The means can be configured to access or otherwise obtain map data associated with a surrounding geographic environment of the autonomous vehicle. More particularly, in some implementations, the means can be configured to access or otherwise obtain map data that provides information regarding: the identity and location of different roadways, road segments, buildings, or other items or objects (e.g., lampposts, crosswalks and/or curb); the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway or other travel way and/or one or more boundary markings associated therewith); traffic control data (e.g., the location and instructions of signage, traffic lights, or other traffic control devices); and/or any other map data that provides information that assists the vehicle computing system in processing, analyzing, and perceiving its surrounding environment and its relationship thereto. In some implementations, the means can be configured to access or otherwise obtain map data that is provided in a birds-eye view representation, such as generated by rasterization or other suitable processing format. A map data obtaining unit is one example of a means for obtaining such map data as described herein.

The means can be configured to provide, as input to a machine-learned object detection model, data describing the sensor data, and to receive the object detection data as an output of the machine-learned object detection. A machine learned object detection model unit is one example of a means for providing the sensor data and map data as inputs to the machine learned object detection model and receiving multiple outputs therefrom.

The means can be configured to generate motion forecast data that describes or predicts the trajectory/behavior of a plurality of actors with respect to the autonomous vehicle. The trajectory/behavior forecasting unit(s) is one example of a means for providing data output from the interaction transformer model(s) to the prediction model(s) (e.g., including the attention model and/or recurrent model and receiving multiple outputs therefrom).

The means can be configured to determine a motion plan for the autonomous vehicle based at least in part on the motion forecast data. The means can be configured to determine a motion plan for the autonomous vehicle that best navigates the autonomous vehicle along a determined travel route relative to the objects at such locations. In some implementations, the means can be configured to determine a cost function for each of one or more candidate motion plans for the autonomous vehicle based at least in part on the current locations and/or predicted future locations and/or moving paths of the objects. A motion planning unit is one example of a means for determining a motion plan for the autonomous vehicle.

The means can be configured to control one or more vehicle controls (e.g., actuators or other devices that control gas flow, steering, braking, etc.) to execute the selected motion plan. A vehicle controlling unit is one example of a means for controlling motion of the autonomous vehicle to execute the motion plan.

With reference now to the FIGS., example aspects of the present disclosure will be discussed in further detail. FIG. 1 illustrates an example vehicle computing system 100 according to example embodiments of the present disclosure. The vehicle computing system 100 can be associated with a vehicle 105. The vehicle computing system 100 can be located onboard (e.g., included on and/or within) the vehicle 105.

The vehicle 105 incorporating the vehicle computing system 100 can be various types of vehicles. The vehicle 105 can be an autonomous vehicle. For instance, the vehicle 105 can be a ground-based autonomous vehicle such as an autonomous car, autonomous truck, autonomous bus, etc. The vehicle 105 can be an air-based autonomous vehicle (e.g., airplane, helicopter, or other aircraft) or other types of vehicles (e.g., watercraft, etc.). The vehicle 105 can drive, navigate, operate, etc. with minimal and/or no interaction from a human operator 106 (e.g., driver). An operator 106 (also referred to as a vehicle operator) can be included in the vehicle 105 and/or remote from the vehicle 105. In some implementations, the vehicle 105 can be a non-autonomous vehicle.

In some implementations, the vehicle 105 can be configured to operate in a plurality of operating modes. The vehicle 105 can be configured to operate in a fully autonomous (e.g., self-driving) operating mode in which the vehicle 105 is controllable without user input (e.g., can drive and navigate with no input from a vehicle operator present in the vehicle 105 and/or remote from the vehicle 105). The vehicle 105 can operate in a semi-autonomous operating mode in which the vehicle 105 can operate with some input from a vehicle operator present in the vehicle 105 (and/or a human operator that is remote from the vehicle 105). The vehicle 105 can enter into a manual operating mode in which the vehicle 105 is fully controllable by a vehicle operator 106 (e.g., human driver, pilot, etc.) and can be prohibited and/or disabled (e.g., temporary, permanently, etc.) from performing autonomous navigation (e.g., autonomous driving). In some implementations, the vehicle 105 can implement vehicle operating assistance technology (e.g., collision mitigation system, power assist steering, etc.) while in the manual operating mode to help assist the vehicle operator of the vehicle 105. For example, a collision mitigation system can utilize a predicted intention of objects within the vehicle's 105 surrounding environment to assist an operator 106 in avoiding collisions and/or delays even when in manual mode.

The operating modes of the vehicle 105 can be stored in a memory onboard the vehicle 105. For example, the operating modes can be defined by an operating mode data structure (e.g., rule, list, table, etc.) that indicates one or more operating parameters for the vehicle 105, while in the particular operating mode. For example, an operating mode data structure can indicate that the vehicle 105 is to autonomously plan its motion when in the fully autonomous operating mode. The vehicle computing system 100 can access the memory when implementing an operating mode.

The operating mode of the vehicle 105 can be adjusted in a variety of manners. For example, the operating mode of the vehicle 105 can be selected remotely, off-board the vehicle 105. For example, a remote computing system (e.g., of a vehicle provider and/or service entity associated with the vehicle 105) can communicate data to the vehicle 105 instructing the vehicle 105 to enter into, exit from, maintain, etc. an operating mode. For example, in some implementations, the remote computing system can be an operations computing system 195, as disclosed herein. By way of example, such data communicated to a vehicle 105 by the operations computing system 195 can instruct the vehicle 105 to enter into the fully autonomous operating mode. In some implementations, the operating mode of the vehicle 105 can be set onboard and/or near the vehicle 105. For example, the vehicle computing system 100 can automatically determine when and where the vehicle 105 is to enter, change, maintain, etc. a particular operating mode (e.g., without user input). Additionally, or alternatively, the operating mode of the vehicle 105 can be manually selected via one or more interfaces located onboard the vehicle 105 (e.g., key switch, button, etc.) and/or associated with a computing device proximate to the vehicle 105 (e.g., a tablet operated by authorized personnel located near the vehicle 105). In some implementations, the operating mode of the vehicle 105 can be adjusted by manipulating a series of interfaces in a particular order to cause the vehicle 105 to enter into a particular operating mode.

The vehicle computing system 100 can include one or more computing devices located onboard the vehicle 105. For example, the computing device(s) can be located on and/or within the vehicle 105. The computing device(s) can include various components for performing various operations and functions. For instance, the computing device(s) can include one or more processors and one or more tangible, non-transitory, computer readable media (e.g., memory devices, etc.). The one or more tangible, non-transitory, computer readable media can store instructions that when executed by the one or more processors cause the vehicle 105 (e.g., its computing system, one or more processors, etc.) to perform operations and functions, such as those described herein for determining object intentions based on physical attributes.

The vehicle 105 can include a communications system 120 configured to allow the vehicle computing system 100 (and its computing device(s)) to communicate with other computing devices. The vehicle computing system 100 can use the communications system 120 to communicate with one or more computing device(s) that are remote from the vehicle 105 over one or more networks (e.g., via one or more wireless signal connections). In some implementations, the communications system 120 can allow communication among one or more of the system(s) on-board the vehicle 105. The communications system 120 can include any suitable components for interfacing with one or more network(s), including, for example, transmitters, receivers, ports, controllers, antennas, and/or other suitable components that can help facilitate communication.

As shown in FIG. 1 , the vehicle 105 can include one or more vehicle sensors 125, an autonomy computing system 130, one or more vehicle control systems 135, and other systems, as described herein. One or more of these systems can be configured to communicate with one another via a communication channel. The communication channel can include one or more data buses (e.g., controller area network (CAN)), on-board diagnostics connector (e.g., OBD-II), and/or a combination of wired and/or wireless communication links. The onboard systems can send and/or receive data, messages, signals, etc. amongst one another via the communication channel.

The vehicle sensor(s) 125 can be configured to acquire sensor data 140. This can include sensor data associated with the surrounding environment of the vehicle 105. For instance, the sensor data 140 can include image and/or other data within a field of view of one or more of the vehicle sensor(s) 125. The vehicle sensor(s) 125 can include a Light Detection and Ranging (LIDAR) system, a Radio Detection and Ranging (RADAR) system, one or more cameras (e.g., visible spectrum cameras, infrared cameras, etc.), motion sensors, and/or other types of imaging capture devices and/or sensors. The sensor data 140 can include image data, radar data, LIDAR data, and/or other data acquired by the vehicle sensor(s) 125. The vehicle 105 can also include other sensors configured to acquire data associated with the vehicle 105. For example, the vehicle 105 can include inertial measurement unit(s), wheel odometry devices, and/or other sensors.

In some implementations, the sensor data 140 can be indicative of one or more objects within the surrounding environment of the vehicle 105. The object(s) can include, for example, vehicles, pedestrians, bicycles, and/or other objects. The object(s) can be located in front of, to the rear of, to the side of the vehicle 105, etc. The sensor data 140 can be indicative of locations associated with the object(s) within the surrounding environment of the vehicle 105 at one or more times. The vehicle sensor(s) 125 can provide the sensor data 140 to the autonomy computing system 130.

In addition to the sensor data 140, the autonomy computing system 130 can retrieve or otherwise obtain map data 145. The map data 145 can provide information about the surrounding environment of the vehicle 105. In some implementations, the vehicle 105 can obtain detailed map data that provides information regarding: the identity and location of different roadways, road segments, buildings, or other items or objects (e.g., lampposts, crosswalks, curbing, etc.); the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway or other travel way and/or one or more boundary markings associated therewith); traffic control data (e.g., the location and instructions of signage, traffic lights, or other traffic control devices); the location of obstructions (e.g., roadwork, accidents, etc.); data indicative of events (e.g., scheduled concerts, parades, etc.); and/or any other map data that provides information that assists the vehicle 105 in comprehending and perceiving its surrounding environment and its relationship thereto. In some implementations, the vehicle computing system 100 can determine a vehicle route for the vehicle 105 based at least in part on the map data 145.

The vehicle 105 can include a positioning system 150. The positioning system 150 can determine a current position of the vehicle 105. The positioning system 150 can be any device or circuitry for analyzing the position of the vehicle 105. For example, the positioning system 150 can determine position by using one or more of inertial sensors (e.g., inertial measurement unit(s), etc.), a satellite positioning system, based on IP address, by using triangulation and/or proximity to network access points or other network components (e.g., cellular towers, WiFi access points, etc.) and/or other suitable techniques. The position of the vehicle 105 can be used by various systems of the vehicle computing system 100 and/or provided to a remote computing system. For example, the map data 145 can provide the vehicle 105 relative positions of the elements of a surrounding environment of the vehicle 105. The vehicle 105 can identify its position within the surrounding environment (e.g., across six axes, etc.) based at least in part on the map data 145. For example, the vehicle computing system 100 can process the sensor data 140 (e.g., LIDAR data, camera data, etc.) to match it to a map of the surrounding environment to get an understanding of the vehicle's position within that environment.

The autonomy computing system 130 can include a perception system 155, a prediction system 160, a motion planning system 165, and/or other systems that cooperate to perceive the surrounding environment of the vehicle 105 and determine a motion plan for controlling the motion of the vehicle 105 accordingly. For example, the autonomy computing system 130 can obtain the sensor data 140 from the vehicle sensor(s) 125, process the sensor data 140 (and/or other data) to perceive its surrounding environment, predict the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment. The autonomy computing system 130 can communicate with the one or more vehicle control systems 135 to operate the vehicle 105 according to the motion plan.

The vehicle computing system 100 (e.g., the autonomy computing system 130) can identify one or more objects that are proximate to the vehicle 105 based at least in part on the sensor data 140 and/or the map data 145. For example, the vehicle computing system 100 (e.g., the perception system 155) can process the sensor data 140, the map data 145, etc. to obtain perception data 170. The vehicle computing system 100 can generate perception data 170 that is indicative of one or more states (e.g., current and/or past state(s)) of a plurality of objects that are within a surrounding environment of the vehicle 105. For example, the perception data 170 for each object can describe (e.g., for a given time, time period) an estimate of the object's: current and/or past location (also referred to as position); current and/or past speed/velocity; current and/or past acceleration; current and/or past heading; current and/or past orientation; size/footprint (e.g., as represented by a bounding shape); class (e.g., pedestrian class vs. vehicle class vs. bicycle class), the uncertainties associated therewith, and/or other state information. The perception system 155 can provide the perception data 170 to the prediction system 160, the motion planning system 165, the interaction system 185, and/or other system(s).

The prediction system 160 can be configured to predict a motion of the object(s) within the surrounding environment of the vehicle 105. For instance, the prediction system 160 can generate prediction data 175 associated with such object(s). The prediction data 175 can be indicative of one or more predicted future locations of each respective object. For example, the prediction system 160 can determine a predicted motion trajectory along which a respective object is predicted to travel over time. A predicted motion trajectory can be indicative of a path that the object is predicted to traverse and an associated timing with which the object is predicted to travel along the path. The predicted path can include and/or be made up of a plurality of way points. In some implementations, the prediction data 175 can be indicative of the speed and/or acceleration at which the respective object is predicted to travel along its associated predicted motion trajectory. In some implementations, the prediction data 175 can include a predicted object intention (e.g., a right turn) based on physical attributes of the object. The prediction system 160 can output the prediction data 175 (e.g., indicative of one or more of the predicted motion trajectories) to the motion planning system 165.

The vehicle computing system 100 (e.g., the motion planning system 165) can determine a motion plan 180 for the vehicle 105 based at least in part on the perception data 170, the prediction data 175, and/or other data. A motion plan 180 can include vehicle actions (e.g., planned vehicle trajectories, speed(s), acceleration(s), intention, other actions, etc.) with respect to one or more of the objects within the surrounding environment of the vehicle 105 as well as the objects' predicted movements. For instance, the motion planning system 165 can implement an optimization algorithm, model, etc. that considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan 180. The motion planning system 165 can determine that the vehicle 105 can perform a certain action (e.g., pass an object, etc.) without increasing the potential risk to the vehicle 105 and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage, etc.). For instance, the motion planning system 165 can evaluate one or more of the predicted motion trajectories of one or more objects during its cost data analysis as it determines an optimized vehicle trajectory through the surrounding environment. The motion planning system 165 can generate cost data associated with such trajectories. In some implementations, one or more of the predicted motion trajectories may not ultimately change the motion of the vehicle 105 (e.g., due to an overriding factor). In some implementations, the motion plan 180 may define the vehicle's motion such that the vehicle 105 avoids the object(s), reduces speed to give more leeway to one or more of the object(s), proceeds cautiously, performs a stopping action, etc.

The motion planning system 165 can be configured to continuously update the vehicle's motion plan 180 and a corresponding planned vehicle motion trajectory. For example, in some implementations, the motion planning system 165 can generate new motion plan(s) for the vehicle 105 (e.g., multiple times per second). Each new motion plan can describe a motion of the vehicle 105 over the next planning period (e.g., next several seconds). Moreover, a new motion plan may include a new planned vehicle motion trajectory. Thus, in some implementations, the motion planning system 165 can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle 105.

The vehicle computing system 100 can cause the vehicle 105 to initiate a motion control in accordance with at least a portion of the motion plan 180. A motion control can be an operation, action, etc. that is associated with controlling the motion of the vehicle. For instance, the motion plan 180 can be provided to the vehicle control system(s) 135 of the vehicle 105. The vehicle control system(s) 135 can be associated with a vehicle controller (e.g., including a vehicle interface) that is configured to implement the motion plan 180. The vehicle controller can, for example, translate the motion plan into instructions for the appropriate vehicle control component (e.g., acceleration control, brake control, steering control, etc.). By way of example, the vehicle controller can translate a determined motion plan 180 into instructions to adjust the steering of the vehicle 105 X degrees, apply a certain magnitude of braking force, etc. The vehicle controller (e.g., the vehicle interface) can help facilitate the responsible vehicle control (e.g., braking control system, steering control system, acceleration control system, etc.) to execute the instructions and implement the motion plan 180 (e.g., by sending control signal(s), making the translated plan available, etc.). This can allow the vehicle 105 to autonomously travel within the vehicle's surrounding environment.

As shown in FIG. 1 , the vehicle 105 can include an HMI (Human Machine Interface) 190 that can output data and accept input from the operator 106 of the vehicle 105. For instance, the HMI 190 can include one or more output devices (e.g., speakers, display devices, tactile devices, etc.) such that, in some implementations, the HMI 190 can provide one or more informational prompts to the operator 106 of the vehicle 105. For example, the HMI 190 can be configured to provide prediction data 170 such as a predicted object intention to one or more vehicle operator(s) 106. Additionally, or alternatively, the HMI 190 can include one or more input devices (e.g., buttons, microphones, cameras, etc.) to accept vehicle operator 106 input. In this manner, the HMI 190 can communicate with the vehicle operator 106.

The vehicle computing system 100 can include an interaction system 185. As illustrated in FIG. 1 , the interaction system 185 can be implemented onboard the vehicle 105 (e.g., as a portion of the vehicle computing system 100). Moreover, in some implementations, the interaction system 185 can be remote from the vehicle 105 (e.g., as a portion of an operations computing system 195). The interaction system 185 can determine one or more object intention(s) associated with objects within the surrounding environment of the vehicle 105, as described in greater detail herein. In some implementations, the interaction system 185 can be configured to operate in conjunction with the vehicle autonomy system 130. For example, the interaction system 185 can send data to and receive data from the vehicle autonomy system 130. In some implementations, the interaction system 185 can be included in or otherwise a part of a vehicle autonomy system 130. The interaction system 185 can include software and hardware configured to provide the functionality described herein. In some implementations, the interaction system 185 can be implemented as a subsystem of a vehicle computing system 100. Additionally, or alternatively, the interaction system 185 can be implemented via one or more computing devices that are remote from the vehicle 105. Example interaction system 185 configurations according to example aspects of the present disclosure are discussed in greater detail with respect to FIGS. 2-6 .

The operator 106 can be associated with the vehicle 105 to take manual control of the vehicle, if necessary. For instance, in a testing scenario, a vehicle 105 can be periodically tested with controlled faults that can be injected into an autonomous vehicle's autonomy system 130. This can help the vehicle's response to certain scenarios. A vehicle operator 106 can be located within the vehicle 105 and/or remote from the vehicle 105 to take control of the vehicle 105 (e.g., in the event the fault results in the vehicle exiting from a fully autonomous mode in the testing environment).

Although many examples are described herein with respect to autonomous vehicles, the disclosed technology is not limited to autonomous vehicles. For instance, any vehicle may utilize the technology described herein for determining object intention. For example, a non-autonomous vehicle may utilize aspects of the present disclosure to determine the intention of one or more objects (e.g., vehicles, bicycles, etc.) proximate to a non-autonomous vehicle. Such information may be utilized by a non-autonomous vehicle, for example, to provide informational notifications to an operator of the non-autonomous vehicle. For instance, the non-autonomous vehicle can notify or otherwise warn the operator of the non-autonomous vehicle based on a determined object intention. Additionally, or alternatively, the disclosed technology can be implemented and utilized by other computing systems such as other robotic computing systems.

FIG. 2 depicts an example data flow diagram 200 of an example prediction system 185 according to example implementations of the present disclosure. To facilitate the determination of an object intention associated with an actor (e.g., an actor proximate to the autonomous vehicle) the prediction system 185 can obtain a relative location embedding 140 via network 205. The relative location embedding 140 can describe relative respective locations of a plurality of actors (e.g., vehicles, pedestrians, etc.) with respect to the autonomous vehicle. The relative location embedding 140 can, in some implementations, be based on or otherwise include any data associated with the surrounding environment of the vehicle 105 such as, for example, camera image data and/or Light Detection and Ranging (LIDAR) data. In some implementations the relative location embedding can be generated by another machine-learned model (e.g., an object detection model, etc.).

More particularly, the relative location embedding 140 can encode location as a positional embedding relative to autonomous vehicle as:

R _(ij) =MLP(|x _(j) ^(i)|,|

|, sgn(x _(j) ^(i)), sgn(

),w _(j) ,l _(j),θ_(j) ^(i)),  (4)

where R∈

can be a 3-dimensional matrix and x_(j) ^(i), y_(j) ^(i), and θ_(j) ^(i) are the locations and orientation of actor j and transformed into actor i's local coordinate system, sgn can be the sign function, and w_(j) and i_(j) can encode the actor's size. A two-layer MLP can be used to transform the 7-channel input to the 16-channel embedding.

The prediction system 185 can include one or more interaction transformer models 210 configured to receive the relative location embedding 140, and in response to receipt of the relative location embedding 140, output motion forecast data 230 that describes trajectories and/or movement of a plurality of actors relative to the autonomous vehicle 105 (FIG. 1 ). In some embodiments, the interaction transformer models 210 can include models that are separate and distinct from other systems described above with reference to FIG. 1 . However, it should be understood that the interaction transformer model(s) 210 can be partially or completely included and/or integrated in one or more of the position system 150, perception system 115, prediction system 160, and/or motion planning system 165 described above with reference to FIG. 1 .

In some implementations, the interaction transformer model(s) 210 can include an interaction model and a recurrent model. More particularly, the interaction model can generate an input sequence by projecting each feature of received object detection data to a query and a pair of keys and values, which can each be or include respective vectors. The input sequence can be input into an attention model, and attentional weights can be received as an output of the attention model. The interaction model can generate an attention embedding with respect to the plurality of actors in response to receipt of the attentional weights. In some implementations, a recurrent model can be configured to receive the attention embedding and the relative location embedding 140, and in response, generate motion forecast data with respect to the plurality of actors. More formally, the input sequence, F^(in) can be denoted as:

F ^(in)∈

where each row is a feature vector. The interaction model can use linear projections to get the set of queries, keys, and values as follows;

Q=F ^(in) W ^(Q) ,K=F ^(in) W ^(K) ,V=F ^(in) W ^(V)  (1)

where each of the following are matrices of weights:

W ^(Q)∈

W ^(K)∈

W ^(V)∈

The scaled dot products between the queries and keys can be used to compute the attentional weights. The values for each query can then be aggregated:

$\begin{matrix} {A = {{{softmax}\left( \frac{{QK}^{T}}{\sqrt{d_{k}}} \right)}V}} & (2) \end{matrix}$

where a softmax function can be used to add a sum-to-one normalization to the attentional weights of a query (each row of QK^(T)). The scaling factor

$\frac{1}{\sqrt{d_{k}}}$

can be used to prevent the dot product from being numerically too large. Finally, the model can use a set of non-linear transformations with shortcut connections to perform feature updates with the recurrent model to produce output features, F^(out):

F ^(out)=ResBlock(MLP(A)+F _(in))  (3)

where MLP denotes a Multi-Layer Perceptron which can be applied to each row of A; ResBlock denotes a residual block and can also be applied on each row, and F^(out) has the same shape as F^(in).

A prediction model 215 can receive the motion forecast data 230. Based on the motion forecast data 230, the prediction model 215 (e.g., a multi-layer perceptron, etc.) can output prediction outputs, which can include respective trajectories 230 and respective projected trajectories 245. More formally, a refinement P_(refine) ^((t)) (e.g., respective trajectories 230), and a proposal for t+1 denoted as P_(proposal) ^((t+1)) (e.g., respective projected trajectories 245) can be generated as prediction outputs by the prediction model(s) 215. Both can be parameterized as (dx, dy, sin 2θ, cos 2θ, clsθ), where (dc, dy) can be relative to the detection box center at t=0, and (sin 2θ, cos 2θ, clsθ) can be defined in the same way as the detection output parameterizations. P_(refine) ^((t)) (e.g., respective trajectories 230) can be used as the final motion forecasting output for time step, t, (e.g., the motion forecast data for the current time step, etc.) and P_(proposal) ^((t+1)) (e.g., respective projected trajectories 245) can be used to compute the relative location embedding 140 for the next step (e.g., the subsequent relative location embedding, etc.). F^(out(t)) can also be used as F^(in(t+1)) (e.g., the input sequence for the subsequent time step, etc.). In such fashion, the respective projected trajectories 245 can be received by the network 205 and used to generate a subsequent relative location embedding 140.

FIG. 3A illustrates a simplified flow chart of an example implementation of a method 300 for generating motion forecast data for a plurality of actors with respect to an autonomous vehicle. The method 300 can generally include input data processing 302, object detection 304, and action prediction 306. Action prediction 306 can employ a recurrent interaction motion forecasting model 340. FIG. 3B is a schematic illustration of the recurrent interaction motion forecasting model 340 of FIG. 3A.

Referring to FIG. 3A, the input data processing 302 can include generating a birds-eye-view (BEV) voxel representation 310 of sensor data 140 (FIGS. 1 and 2 ), such as LiDAR point clouds. For example, a LIDAR sweep point cloud can be voxelized into a 3D occupancy grid with a fixed 3D voxel resolution, which can be centered at the position of the autonomous vehicle (ego car position). The point feature can be defined as 1 to represent occupancy. The birds-eye-view (BEV) voxel representation 310 can be computed by summing all nearby point features weighted by their relative positions to the voxel center. As a result, the voxel representation 310 can implicitly preserve fine-grained information within the voxel. In order to capture past motion for the task of prediction, multiple past LiDAR sweeps can be aggregated by concatenating their voxel representations along the Z-axis. The past LiDAR sweeps can be registered to current frame by ego motion compensation. Ego motion compensation generally refers to compensating for movement of the autonomous vehicle to with respect to sensor data. In this example, movement of the autonomous vehicle can be tracked using one or more tracking data sources (e.g., through image frame, accelerometers, or the like). The LiDAR data can be adjusted, reconciled, aligned, and/or mapped with respect to such tracking data to compensate for movement of the autonomous vehicle.

In some implementations, map data (e.g., geometric and/or semantic map priors) can be exploited for better reasoning. For example, ground height can be subtracted from the Z value of each LiDAR point before voxelization. This approach can remove the variation caused by ground slope. Semantic priors can also be extracted in the form of road masks 312 and lane masks 314. Each of them can be a one-channel raster image in BEV depicting the drivable surface and lanes, respectively. The voxel representations 310 can be augmented with semantic map priors 312, 314 by concatenation to generate an input representation 316.

Object detection 304 can be performed on the input representation 316. The output space of the input representation 316 can be defined in BEV, which can provide for efficient feature sharing between perception and prediction. The detection can be parameterized as a set of oriented bounding boxes. A detection box can be denoted as (x_(i), y_(i), w_(i), l_(i), θ_(i)), where x_(i) and y_(i) represent the box center, (w_(i), l_(i)) represent box size, and θ_(i) represents the orientation. Note that the missing Z dimension can be recovered from the ground prior in the HD map. Additionally, the trajectory can be represented as a sequence of boxes at future T time steps, denoted as {(x_(i) ^((t)), y_(i) ^((t)), θ_(i) ^((t)))}, where t=1, . . . , T. The objects can be assumed to be rigid, and thus their sizes can be kept the same across all time steps.

Object detection 304 can employ a sensor-fusion backbone network including a two-stream architecture. A BEV stream network 320 can be or include a 2D convolutional network that extracts features in BEV space from the input representation 316 (e.g., including joint LiDAR and map representations). Inception-like blocks can be stacked sequentially with residual connections to extract multi-scale feature maps. An image stream network 322 can be or include residual blocks that are pre-trained. For instance, the image stream network 322 can be or include a ResNet-18 model that is pre-trained on an ImageNet data set. The image stream network 322 can be configured to receive camera images 324 as input. Multi-scale image feature maps from each ResNet-18 residual block can be aggregated, for example using a feature pyramid network. The aggregated feature map can be fused with the BEV stream via a continuous fusion layer 324. Thus, object detection 304 can employ a two-stream architecture to detect locations of actors with respect to the autonomous vehicle.

Compared with direct feature projection, the continuous fusion layer 324 can provide a dense fusion from image space to BEV space through linear interpolation parameterized by a trainable multi-layer perceptron MLP. More specifically, image features can first be back-projected to BEV space according to the existing LiDAR observation. At BEV locations with no LiDAR point, the image features can be interpolated from nearby occupied locations (e.g., using one or more MLPs). The image feature and BEV feature can be fused together, at 324, by element-wise addition (assuming same number of feature channels) in BEV space. Thus, the BEV stream of LiDAR data can be fused with camera images to generate an output feature map 326.

The output feature map 326 can be input into a machine-learned object detection model 329 (e.g., including a neural network), and a relative location embedding 328 can be received as an output of the machine-learned object detection model 329. The object detection data 328 can include multi-sensor features for use in action prediction 306. More specifically, as vehicles do not overlap and have relatively similar size in BEV space, object detection 329 can be formulated as dense prediction without introducing any object anchors. Several convolutions (e.g., 1×1) can be applied on top of the output feature map 326, which can output an n-dimensional vector per voxel at last, representing a confidence score, s, and a bounding box parameterized as (dc, dy, w, l, sin 2θ, cos 2θ, cls_(θ)), where (dr, dy) represent the relative position offset from the voxel center to the box center; (w, l) is the box size; and (sin 2θ, cos 2θ, cls_(θ)) is used to decode the orientation. (sin 2θ, cos 2θ) can be regressed to get an orientation which doesn't distinguish forward and backward. However, this can effectively model interaction to determine whether a vehicle is facing forward or backward. An additional classification output clsθ can be used to classify whether the orientation is in

$\left( {{\frac{1}{4}\pi},{\frac{3}{4}\pi}} \right).$

This classification target can, in some implementations, be better than (0, π] since orientations of many vehicles are similar or opposite to the orientation of the ego vehicle, which are at the boundaries of (0, π]. Oriented non-maximum suppression (NMS) can be applied to remove the duplicates, and keep all remaining boxes whose score is above a threshold as final detections.

The relative location embedding 328 can be received by the interaction transformer model 330. The interaction transformer model 330 can generate, based on the relative location embedding 328, motion forecast data. The motion forecast data can, in some implementations, include interaction attention from an interaction attention model, as described previously. The prediction model 340 can receive the motion forecast data, and based on the motion forecast data, output respective trajectories 339 for the plurality of actors for a current step (e.g., t=0, etc.), and output respective projected trajectories 339 for a subsequent time step (e.g., t=1, t=2, etc.). Further, the interaction transformer model 330 can, in some implementations, receive the respective trajectories 339 in a subsequent iteration to serve as the relative location embedding 328 input to generate a subsequent respective trajectory 339 (e.g., t=2, etc.).

As an example, for a first iteration at a first time step t=0, the interaction transformer model 330 can receive a first relative location embedding 328 and generate first motion forecast data. The prediction model 340 can receive the first motion forecast data and generate output respective trajectories 339 for the current step t=O and output respective projected trajectories for a subsequent time step t=1.

Following the previous example, at a second iteration at a second time step t=1, a second relative location embedding 328 can be generated based on the previously generated respective projected trajectory 339 for t=1 (e.g., the relative location embedding can account for the actor trajectory differences predicted for t=1). The interaction transformer model 330 can receive the second relative location embedding 328 and output second motion forecast data. The prediction model 340 can receive the second motion forecast data and output a respective trajectory for t=1 and a respective projected trajectory for t=2. In such fashion, the interaction transformer model 300 and the prediction model 340 can operate iteratively to predict subsequent actor trajectories at subsequent time steps.

Aspects of the present disclosure are directed to Recurrent Interactive Motion Forecasting, which can refer to employing recurrent machine-learning models and/or techniques to predict the trajectories of other actors with respect to the autonomous vehicle. Recurrent interaction motion forecasting can provide various benefits. For example, recurrent interaction motion forecasting can 1) jointly reason over all actors to capture their interactions and/or 2) iteratively infer each trajectory to capture the sequential nature of the trajectory. This can allow for the disclosed technology to account for the consideration that the behaviors of actors can heavily depend on each other. For example, a vehicle can modulate speed to maintain a safe distance from the vehicle ahead. In another example, vehicles typically wait for the other vehicles that have the right of way at intersections. Additionally, the recurrent interaction motion forecasting can consider the output trajectory to have a Markovian temporal dependency. That is, the output at each time step depends on the outputs at previous time steps. Thus, recurrent interaction motion forecasting, as described herein can provide various benefits.

FIG. 3B is a schematic illustration of the interaction transformer model of FIG. 3A. Recurrent interaction motion forecasting, as described herein, can have an interaction transformer configuration. A transformer configuration generally refers to a sequence-to-sequence model with an encoder-decoder configuration or architecture. More specifically, an interaction model 341 can include an attention model 342 and a context aggregation model 344 (e.g., in an encoder-decoder configuration). An input sequence 350 can be generated that describes the object detection data. For example, the interaction model 341 can generate the input sequence 350 by projecting each feature of the object detection data to a query and a pair of keys and values, which can each be or include respective vectors. The input sequence 350 can be analyzed using the attention model 342 to generate attentional weights 351. For example, the input sequence 350 can be input into the attention model 342. The attentional weights 351 can be received as an output of the attention model 342. The interaction model 341 can generate an attention embedding 358 with respect to the plurality of actors in response to receipt of the attentional weights 351. Lastly, a recurrent model 346 can be configured to receive the attention embedding 358, and in response to receipt of the attention embedding 358, generate motion forecast data 245 (e.g., included in output features 360) with respect to the plurality of actors.

This configuration can be used to draw global dependencies between input and output, especially for long sequences. More specifically, the interaction model 341 can project each feature to a query 353 and a pair of key 354 and value 356, which can all be or include vectors. For each query 353, the interaction model 341 can compute a set of attentional weights 351 using a compatibility function between the query 353 and the set of keys 356. The output feature can be the sum of values 356 weighted by the attentional weights 351, plus some nonlinear transformations.

More formally, the input sequence, F^(in) (350) can be denoted as:

F ^(in)∈

where each row is a feature vector. The interaction model 341 can use linear projections to get the set of queries 353, keys, 354, and values 356 as follows:

Q=F ^(in) W ^(Q) ,K=F ^(in) W ^(K) ,V=F ^(in) W ^(V)  (1)

where each of the following are matrices of weights:

W ^(Q)∈

W ^(K)∈

W ^(V)∈

The scaled dot products between the queries 353 and keys 354 can be used to compute the attentional weights 351. The values for each query 353 can then be aggregated:

$\begin{matrix} {A = {{{softmax}\left( \frac{{QK}^{T}}{\sqrt{d_{k}}} \right)}V}} & (2) \end{matrix}$

where a softmax function can be used to add a sum-to-one normalization to the attentional weights 351 of a query 353 (each row of QK^(T)). The scaling factor

$\frac{1}{\sqrt{d_{k}}}$

can be used to prevent the dot product from being numerically too large. Finally, Transformer can use a set of non-linear transformations with shortcut connections to perform feature updates with the recurrent model 346 to produce output features, F^(out), 360:

F ^(out)=ResBlock(MLP(A)+F _(in))  (3)

where MLP 362 denotes a Multi-Layer Perceptron which can be applied to each row of A; ResBlock 364 denotes a residual block and can also be applied on each row, and F^(out) (360) has the same shape as F^(in) (350).

As indicated above, the input can correspond with a set of actors and their representations. The state of each actor can be represented with features extracted from the BEV feature map as well as the actor location, which can simply be, for example, the x and y coordinates of the actor center. Information about object size and orientation can be encoded in the feature map and thus may not be explicitly encoded in the actor representation.

To capture the sequential nature of the trajectory outputs, the recurrent interaction motion forecasting model 340 can be used to predict the motion in an auto-regressive way. At each time step, an interaction transformer 330 can be used to update the states of all actors and then predict the next waypoint of the trajectory for each.

More specifically, an actor features matrix, Fin (350), can be defined where each row is a feature vector of a respective actor. Equation (1), above, can be used to compute the queries 353. Instead of encoding location as absolute positional embeddings (which can be fused into the input features), a relative location embedding 352 can be employed:

R _(ij) =MLP(|x _(j) ^(i)|,|

|, sgn(x _(j) ^(i)), sgn(

),w _(j) ,l _(j),θ_(j) ^(i)),  (4)

where R∈

can be a 3-dimensional matrix and x_(j) ^(i), y_(j) ^(i), and θ_(j) ^(i) are the locations and orientation of actor j and transformed into actor i's local coordinate system, sgn can be the sign function, and w_(j) and l_(j) can encode the actor's size. A two-layer MLP can be used to transform the 7-channel input to the 16-channel embedding. F^(in) and R can be used compute K (354) and V (356). Both K (354) and V (356) can be 3-dimensional matrices:

K∈

V∈

K (354) and V (356) can be computed as follows:

K _(i) =MLP(Concat(F ^(in) W ^(K) ¹ ,R _(i) W ^(K) ² ))  (5)

V _(i)=Concat(F ^(in) W ^(V) ¹ ,R _(i) W ^(V) ² )  (6)

In these two equations, Concat denotes concatenation along the second dimension, and MLP can be applied on each row vector. The attention embedding 358, A_(i), can be computed as follows:

$\begin{matrix} {A_{i} = {{{sigmoid}\left( \frac{Q_{i}K_{i}^{T}}{\sqrt{d_{k}}} \right)}V_{i}}} & (7) \end{matrix}$

Note the softmax function from Equation (2) can be changed to a sigmoid function. Further, removing the sum-to-one constraint can lead to better performance in this task.

Finally, the output features, F_(i) ^(out) (360), can be computed using Equation (3), above. More specifically, the attention embedding 358 can be input into the recurrent model 346. Output features 360 that can include motion forecast data can be received as an output of the recurrent model 346. The motion forecast data can describe respective trajectories for the plurality of actors.

To capture the sequential nature of the trajectory outputs, at each time step, the interaction transformer 330 (FIG. 3A) can update the states of all actors and then predict the next waypoint of the trajectory for each actor. The input actor features and relative location embedding at time step t can be denoted as F^(in(t)) (e.g., the input sequence 350 describing object detection data, etc.) and R^((t)) (e.g., the relative location embedding, etc.). The detection bounding boxes can be used to compute R⁽⁰⁾, and set F^(in(0)) to be the bilinearly interpolated output BEV features extracted at the detection box centers. The interaction transformer model 330 can take F^(in(t)) and R^((t)) and output F^(out(t)) (e.g., motion forecast data with respect to the plurality of actors, etc.), which can then be fed into the prediction model 340 to get the prediction outputs.

The prediction outputs can include: (1) a refinement P_(refine) ^((t)) (e.g., respective trajectories for the plurality of actors for a current time step t, etc.), and (2) a proposal for t+1 denoted as P_(proposal) ^((t+1)) (e.g., respective projected trajectories for a subsequent time step, t+1, etc.). Both the respective trajectories and the respective projected trajectories can be parameterized respectively as (dr, dy, sin 2θ, cos 2θ, cls_(θ)), where (dx, dy) can be relative to the detection box center at t=0, and (sin 2θ, cos 2θ, cls_(θ)) can be defined in the same way as the detection output parameterizations. The respective trajectories (e.g., P_(refine) ^((t))) can be used as the final motion forecasting output for a first iteration at a first time step, t, (e.g., the respective trajectories for the actors at the current time step, etc.) and the respective projected trajectories (e.g., P_(proposal) ^((t−1))) can be used to compute the next relative location embedding (R^((t+1))) for a subsequent iteration at a subsequent time step (e.g., a second relative location embedding, etc.). Further, in some implementations, F^(out(t)) 360 can also be used as F^(in(t+1)) (e.g., an input sequence 350 for interaction transformer model in at a second iteration at a second time step, etc.). The prediction model 340 can be used to generate both the respective trajectory (e.g., the proposal) and the respective projected trajectory (e.g., the refinement) for simplicity.

FIG. 4 depicts an example flow diagram of an example method 400 for iteratively generating respective trajectories for a plurality of actors with respect to an autonomous vehicle. One or more portion(s) of the method 400 can be can be implemented by a computing system that includes one or more computing devices such as, for example, the computing systems described with reference to the other figures (e.g., the vehicle computing system 100, the interaction system 185, the operations computing system 195, etc.). Each respective portion of the method 400 can be performed by any (or any combination) of one or more computing devices. Moreover, one or more portion(s) of the method 400 can be implemented as an algorithm on the hardware components of the device(s) described herein (e.g., as in FIGS. 1 through 3B), for example, to determine respective trajectories 230 for the plurality of actors. FIG. 4 depicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, and/or modified in various ways without deviating from the scope of the present disclosure. FIG. 4 is described with reference to elements/terms described with respect to other systems and figures for example illustrated purposes and is not meant to be limiting. One or more portions of method 400 can be performed additionally, or alternatively, by other systems.

It should be noted that, in some implementations, one or more portions of method 400 can be performed over a number of iterations. More particularly, as depicted, steps 405-420 of method 400 can occur at a first iteration 401 and steps 425-435 can occur at a second iteration 402. In such fashion, the method can be utilized recurrently by the computing system.

At (405), the method 400 can include inputting a first relative location embedding into an interaction transformer model configured to receive a relative location embedding and generate motion forecast data. For instance, an autonomous vehicle (e.g., vehicle 105) can obtain sensor data associated with a surrounding environment of the autonomous vehicle (e.g., vehicle 105) via one or more vehicle sensors. A relative location embedding can be generated based at least in part on the sensor data. A computing system (e.g., vehicle computing system, robotic system, etc.) can input data that describes relative location embedding into one or more interaction transformer model(s). The sensor data obtaining unit(s) 705, trajectory forecasting unit 715, and object detection unit 725 described below with reference to FIG. 7 are example means for obtaining the first relative location embedding. The trajectory forecasting unit 715 is one example means for inputting the first relative location embedding into the interaction transformer model.

At (410), the method 400 can include receiving, as an output of the interaction transformer model, first motion forecast data for a first plurality of actors at the first time step. For example, the motion forecast data can describe trajectories and/or movement of a plurality of actors relative to the autonomous vehicle. Additional examples of features of the plurality of the actors that can be described by the motion forecast data include orientation data, velocity data, size data, hidden layer data from one or more embedded layers of the motion forecast data, and/or any other suitable data describing the plurality of actors relative to the autonomous vehicle. The computing system (e.g., a vehicle computing system) can receive, as the output of the object detection model(s), the motion forecast data that describes the features of the plurality of the actors relative to the autonomous vehicle. The trajectory forecasting unit 715 and the storing unit 740 as described below with reference to FIG. 7 are example means for receiving, as an output of the interaction transformer model, first motion forecast data for the plurality of actors at the first time step.

At (415), the method 400 can include inputting the first motion forecast data into a prediction model. The prediction model can be configured to generate respective trajectories for the plurality of actors for a current time step and respective projected trajectories for a subsequent time step. More particularly, the prediction model can generate a current trajectory prediction for each of the actors in the environment external to the autonomous vehicle (e.g., a respective trajectory) while also generating a subsequent trajectory prediction for each of the actors in the environment external to the autonomous vehicle (e.g., respective projected trajectories). In such fashion, the prediction model can generate two outputs that serve as current and subsequent predictions for the actions of nearby actors. The trajectory forecasting unit 715 as described below with reference to FIG. 7 is one example means for inputting the first motion forecast data into a prediction model.

At (420), the method 400 can include receiving first respective trajectories of the plurality of actors for the first time step and respective projected trajectories for a second time step. As described previously, the respective trajectories can describe trajectory predictions for actors in the environment external to the autonomous vehicle at a first time step (e.g., t=0). Similarly, the respective projected trajectories can describe trajectory predictions for actors in the environment external to the autonomous vehicle at a second time step (e.g., t=1). In such fashion, the prediction model can sequentially predict actor trajectories at discrete time steps (e.g., t=0 and t=1), while also providing subsequent actor predicted trajectories (e.g., t=1, t=2) that can be used to generate a subsequent input to the interaction transformer model (e.g., to calculate a respective location embedding, etc.). As an example, the prediction model can generate respective trajectories for a current time t=O and respective projected trajectories for a subsequent time step t=1. The respective trajectory for t=1 can be used to generate a relative location embedding for time step t=1. The trajectory forecasting unit 715 and the storing unit 740 as described below with reference to FIG. 7 are example means for receiving first respective trajectories of the plurality of actors for the first time step and respective projected trajectories for a second time step.

At (425), the method 400 can include generating a second relative location embedding for the second time step based on the respective projected trajectories for the second time step. More particularly, the respective projected trajectories for the second time step can describe predicted trajectories for the actors external to the autonomous vehicle at future time. Based on these predicted trajectories, the computing system of the autonomous vehicle can generate a second respective location embedding. In some implementations, the computing system of the autonomous vehicle can use one or more machine-learned models to generate the second respective location embedding (e.g., an object detection model, an interaction model, etc.). The trajectory forecasting unit 715 and the storing unit 740 as described below with reference to FIG. 7 are example means for generating a second relative location embedding for the second time step based on the respective projected trajectories for the second time step.

At (430), the method 400 can include analyzing the second relative location embedding using the interaction transformer model. This analysis can produce second motion forecast data for the plurality of actors at the second time step. For example, the motion forecast data can describe the respective trajectories and the method 400 can include analyzing the motion forecast data to interpret or transform the motion forecast data into time-based location points representing the respective trajectories. The computing system (e.g., vehicle computing system, robotic system, etc.) can determine the respective trajectories for the plurality of actors based on motion forecast data (e.g., output features 360). The trajectory forecasting unit 715 as described below with reference to FIG. 7 is one example means for analyzing the second relative location embedding using the interaction transformer model.

At (435), the method 400 can include determining second respective trajectories of the plurality of actors for the second time step using the prediction model based on the second motion forecast data. As described previously, the respective trajectories can describe trajectory predictions for actors in the environment external to the autonomous vehicle at a first time step (e.g., a “current” time step). In some implementations, second respective projected trajectories can further be determined at (435). The second respective projected trajectories can describe trajectory predictions for actors in the environment external to the autonomous vehicle at a third time step (e.g., a “subsequent” time step). In such fashion, the prediction model can operate recurrently by continuously producing current actor prediction outputs while also generating information (e.g., respective projected trajectories) that is used to generate inputs (e.g., respective location embeddings) for the interaction transformer module. The trajectory forecasting unit 715 and the storing unit 740 as described below with reference to FIG. 7 are example means for determining second respective trajectories of the plurality of actors for the second time step using the prediction model based on the second motion forecast data.

FIG. 5 depicts an example flow diagram of an example method 500 for training a machine-learned interaction transformer model according to example implementations of the present disclosure. One or more portion(s) of the method 500 can be implemented by a computing system that includes one or more computing devices such as, for example, the computing systems described with reference to the other figures (e.g., the vehicle computing system 100, the interaction system 185, the operations computing system 195, etc.). Each respective portion of the method actors can be performed by any (or any combination) of one or more computing devices. Moreover, one or more portion(s) of the method actors can be implemented as an algorithm on the hardware components of the device(s) described herein (e.g., as in FIGS. 1-4 ), and/or on a training computing system accessible by a network. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, and/or modified in various ways without deviating from the scope of the present disclosure. FIG. 5 is described with reference to elements/terms described with respect to other systems and figures for example illustrated purposes and is not meant to be limiting. One or more portions of method actors can be performed additionally, or alternatively, by other systems.

The method 500 can include steps 505, 510, 515, 520, 525, 530, and 535 corresponding with 405, 410, 415, 420, 425, 430, and 435 described above with reference to FIG. 4 . The method 500 can further include, at (540), updating one or more parameters of the interaction transformer model, the prediction model, and/or any other machine-learned models utilized (e.g., object detection model(s), attention model(s), recurrent model(s), interaction model(s), transformer model(s), etc.). In some implementations, two or more of the models can be trained in an end-to-end configuration. For example, errors can be sequentially back-propagated through each of the interaction transformer model and the prediction model to evaluate a joint loss function. A gradient of the joint loss function can be calculated to determine adjust the parameter(s) to reduce the joint loss function to jointly train the models. The trajectory forecasting unit 715 and the storing unit 740 as described below with reference to FIG. 7 are example means for updating one or more parameters of the interaction transformer model, the prediction model, and/or any other machine-learned models utilized.

For example, the interaction transformer and prediction model(s) can be fully differentiable and thus can be trainable end-to-end through back-propagation. More particularly, a loss function can evaluate a difference between respective ground truth trajectories for the plurality of actors and the second respective trajectories for the plurality of actors at the second time step. As an example, detection loss can represent the weighted sum of object classification, detection box regression, and orientation classification losses based on differences between the associated ground truths and the second respective trajectories. Prediction loss can represent the box center and orientation regression, as well as orientation classification losses for all future prediction steps based on differences between the associated ground truths and the second respective trajectories. Prediction loss for proposal and refinement can also summed together. For example, binary cross entropy can be utilized as the classification loss and smooth L1-loss can be used as the regression loss.

In some implementations, hard negative mining can be employed. For object detection, the distance between BEV voxels and their closest ground-truth box centers can be used to determine positive and negative samples. Samples having distances smaller than a threshold can be considered as positive. Samples having distances larger than the threshold can be considered as negative. As a large proportion of the samples are negative in dense object detection, online hard negative mining can be employed. In some implementations, only the most difficult negative samples (with largest loss) can be kept and easy negative samples can be ignored. Classification loss can be averaged over both positive and negative samples while regression loss can be averaged over positive samples only.

In some implementations, online association can be performed between detection results and ground truth labels to compute prediction loss. For each detection, the ground-truth box can be assigned with the maximum (oriented) intersection of union IoU. If a ground truth box is assigned to multiple detections, only the detection with maximum IoU can be kept while other detections are ignored. Regression on future motion can then be averaged over those detections with the associated ground-truth.

FIG. 6 depicts example system components of an example system 600 according to example implementations of the present disclosure. The example system 600 illustrated in FIG. 6 is provided as an example only. The components, systems, connections, and/or other aspects illustrated in FIG. 6 are optional and are provided as examples of what is possible, but not required, to implement the present disclosure. The example system 600 can include an interaction system 185 and a machine learning computing system 650 that are communicatively coupled over one or more network(s) 640. As described herein, the interaction system 185 can be implemented onboard a vehicle (e.g., as a portion of the vehicle computing system 100) and/or can be remote from a vehicle (e.g., as a portion of an operations computing system 195). In either case, a vehicle computing system 100 can utilize the operations and model(s) of the interaction system 185 (e.g., locally, via wireless network communication, etc.).

The interaction system 185 can include one or computing device(s) 610. The computing device(s) 610 of the interaction system 185 can include processor(s) 615 and a memory 620. The one or more processor(s) 615 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 620 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and/or combinations thereof.

The memory 620 can store information that can be obtained by the one or more processor(s) 615. For instance, the memory 620 (e.g., one or more non-transitory computer-readable storage mediums, memory devices, etc.) can include computer-readable instructions 625 that can be executed by the one or more processors 615. The instructions 625 can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions 625 can be executed in logically and/or virtually separate threads on processor(s) 615.

For example, the memory 620 can store instructions 625 that when executed by the one or more processors 615 cause the one or more processors 615 (e.g., of the interaction system 185) to perform operations such as any of the operations and functions of the interaction system 185 and/or for which the interaction system 185 is configured, as described herein, the operations for forecasting actor motion (e.g., one or more portions of method 400), the operations for training a model to recurrently generate trajectory predictions (e.g., one or more portions of method 500), the operations and functions of any of the models described herein, and/or for which the models are configured and/or any other operations and functions for the interaction system 185, as described herein.

The memory 620 can store data 630 that can be obtained (e.g., received, accessed, written, manipulated, generated, created, stored, etc.). The data 630 can include, for instance, sensor data, object detection data, input sequence(s), input representation(s), attention embedding(s), motion forecast data, data indicative of machine-learned model(s) (e.g., the object detection model, attention model, and/or recurrent model), and/or other data/information described herein. In some implementations, the computing device(s) 610 can obtain data from one or more memories that are remote from the interaction system 185.

The computing device(s) 610 can also include a communication interface 635 used to communicate with one or more other system(s) (e.g., other systems onboard and/or remote from a vehicle, the other systems of FIG. 1 , etc.). The communication interface 635 can include any circuits, components, software, etc. for communicating via one or more networks (e.g., 645). In some implementations, the communication interface 635 can include, for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software and/or hardware for communicating data/information.

According to an aspect of the present disclosure, the interaction system 185 can store or include one or more machine-learned models 640. As examples, the machine-learned model(s) 640 can be or can otherwise include the interaction transformer model(s) 210 and/or the prediction model(s) 215. The machine-learned model(s) 640 can be or include neural networks (e.g., deep neural networks), support vector machines, decision trees, ensemble models, k-nearest neighbors models, Bayesian networks, or other types of models including linear models and/or non-linear models. Example neural networks include recurrent neural networks (e.g., long short-term memory recurrent neural networks, etc.), feed-forward neural networks (e.g., convolutional neural networks, etc.), and/or other forms of neural networks.

In some implementations, the interaction system 185 can receive the one or more machine-learned models 640 (e.g., the interaction transformer model(s), attention model(s), recurrent model(s), prediction model(s), etc.), from the machine learning computing system 650 over the network(s) 645 and can store the one or more machine-learned models 640 in the memory 620 of the interaction system 185. The interaction system 185 can use or otherwise implement the one or more machine-learned models 640 (e.g., by processor(s) 615). In particular, the interaction system 185 can implement the machine learned model(s) 640 to forecast actor motion based on sensor data (and optionally map data), as described herein.

The machine learning computing system 650 can include one or more processors 655 and a memory 665. The one or more processors 655 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 665 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and/or combinations thereof.

The memory 665 can store information that can be accessed by the one or more processors 655. For instance, the memory 665 (e.g., one or more non-transitory computer-readable storage mediums, memory devices, etc.) can store data 675 that can be obtained (e.g., generated, retrieved, received, accessed, written, manipulated, created, stored, etc.). In some implementations, the machine learning computing system 650 can obtain data from one or more memories that are remote from the machine learning computing system 650.

The memory 665 can also store computer-readable instructions 670 that can be executed by the one or more processors 655. The instructions 670 can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions 670 can be executed in logically and/or virtually separate threads on processor(s) 655. The memory 665 can store the instructions 670 that when executed by the one or more processors 655 cause the one or more processors 655 to perform operations. The machine learning computing system 650 can include a communication interface 660, including devices and/or functions similar to that described with respect to the interaction system 185.

In some implementations, the machine learning computing system 650 can include one or more server computing devices. If the machine learning computing system 650 includes multiple server computing devices, such server computing devices can operate according to various computing architectures, including, for example, sequential computing architectures, parallel computing architectures, or some combination thereof.

In addition, or alternatively to the model(s) 640 at the interaction system 185, the machine learning computing system 650 can include one or more machine-learned model(s) 680. As examples, the machine-learned model(s) 680 can be or can otherwise include various machine-learned models such as, for example, neural networks (e.g., deep neural networks), support vector machines, decision trees, ensemble models, k-nearest neighbors models, Bayesian networks, or other types of models including linear models and/or non-linear models. Example neural networks include feed-forward neural networks (e.g., convolutional neural networks), recurrent neural networks (e.g., long short-term memory recurrent neural networks, etc.), and/or other forms of neural networks. The machine-learned models 680 can be similar to and/or the same as the machine-learned models 640, and/or any of the models discussed herein with reference to FIGS. 1 through 3B.

As an example, the machine learning computing system 650 can communicate with the interaction system 185 according to a client-server relationship. For example, the machine learning computing system 650 can implement the machine-learned models 680 to provide a web service to the interaction system 185 (e.g., including on a vehicle, implemented as a system remote from the vehicle, etc.). For example, the web service can provide machine-learned models to an entity associated with a vehicle; such that the entity can implement the machine-learned model(s) (e.g., to forecast actor motion, etc.). Thus, machine-learned models 680 can be located and used at the interaction system 185 (e.g., on the vehicle 105, at the operations computing system 195, etc.) and/or the machine-learned models 680 can be located and used at the machine learning computing system 650.

In some implementations, the machine learning computing system 650 and/or the interaction system 185 can train the machine-learned model(s) 640 and/or 680 through the use of a model trainer 685. The model trainer 685 can train the machine-learned models 640 and/or 680 using one or more training or learning algorithm(s), for example as described above with reference to FIG. 5 . The model trainer 785 can perform backwards propagation of errors, supervised training techniques using a set of labeled training data, and/or unsupervised training techniques using a set of unlabeled training data. The model trainer 685 can perform a number of generalization techniques to improve the generalization capability of the models being trained. Generalization techniques include weight decays, dropouts, or other techniques.

The model trainer 680 can train a machine-learned model (e.g., 640 and/or 680) based on a set of training data 690. The training data 690 can include, for example, labeled datasets and/or unlabeled datasets.

In some implementations, the training data 690 can be taken from the same vehicle as that which utilizes the model(s) 640 and/or 680. Accordingly, the model(s) 640 and/or 680 can be trained to determine outputs in a manner that is tailored to that particular vehicle. Additionally, or alternatively, the training data 690 can be taken from one or more different vehicles than that which is utilizing the model(s) 640 and/or 680. The model trainer 685 can be implemented in hardware, firmware, and/or software controlling one or more processors. Additionally, or alternatively, other data sets can be used to train the model(s) (e.g., models 640 and/or 680) including, for example, publicly accessible datasets (e.g., labeled data sets, unlabeled data sets, etc.).

The network(s) 645 can be any type of network or combination of networks that allows for communication between devices. In some embodiments, the network(s) 645 can include one or more of a local area network, wide area network, the Internet, secure network, cellular network, mesh network, peer-to-peer communication link and/or some combination thereof and can include any number of wired or wireless links. Communication over the network(s) 645 can be accomplished, for instance, via a network interface using any type of protocol, protection scheme, encoding, format, packaging, etc.

FIG. 6 illustrates one example system 600 that can be used to implement the present disclosure. Other computing systems can be used as well. For example, in some implementations, the interaction system 185 can include the model trainer 685 and the training dataset 690. In such implementations, the machine-learned models 640 can be both trained and used locally at the interaction system 185 (e.g., at the vehicle 105).

Computing tasks discussed herein as being performed at computing device(s) remote from the vehicle 105 can instead be performed at the vehicle 105 (e.g., via the vehicle computing system 100), or vice versa. Such configurations can be implemented without deviating from the scope of the present disclosure. The use of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. Computer-implemented operations can be performed on a single component or across multiple components. Computer-implemented tasks and/or operations can be performed sequentially or in parallel. Data and instructions can be stored in a single memory device or across multiple memory devices.

FIG. 7 depicts example system components of an example system according to example implementations of the present disclosure. Various means can be configured to perform the methods and processes described herein. For example, a computing system 700 can include sensor data obtaining unit(s) 705, map data obtaining unit(s) 710, machine-learned object recognition/detection model application unit(s) 725, trajectory/behavior forecasting unit(s) 715, vehicle controlling unit(s) 720, operator communication unit(s) 730, data storing unit(s) 740, and/or other means for performing the operations and functions described herein. In some implementations, one or more of the units may be implemented separately. In some implementations, one or more units may be a part of or included in one or more other units. These means can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s), dedicated circuit(s), application-specific integrated circuit(s), programmable array logic, field-programmable gate array(s), controller(s), microcontroller(s), and/or other suitable hardware. The means can also, or alternately, include software control means implemented with a processor or logic circuitry for example. The means can include or otherwise be able to access memory such as, for example, one or more non-transitory computer-readable storage media, such as random-access memory, read-only memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, flash/other memory device(s), data registrar(s), database(s), and/or other suitable hardware.

The means can be programmed to perform one or more algorithm(s) for carrying out the operations and functions described herein. For instance, the means can be configured to obtain sensor data from one or more sensors that generate sensor data relative to an autonomous vehicle. In some implementations, the means can be configured to obtain sensor data associated with the autonomous vehicle's surrounding environment as well as the position and movement of the autonomous vehicle. A sensor data obtaining unit 705 is one example of a means for obtaining such sensor data as described herein.

The means can be configured to access or otherwise obtain map data associated with a surrounding geographic environment of the autonomous vehicle. More particularly, in some implementations, the means can be configured to access or otherwise obtain map data that provides information regarding: the identity and location of different roadways, road segments, buildings, or other items or objects (e.g., lampposts, crosswalks and/or curb); the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway or other travel way and/or one or more boundary markings associated therewith); traffic control data (e.g., the location and instructions of signage, traffic lights, or other traffic control devices); and/or any other map data that provides information that assists the vehicle computing system in processing, analyzing, and perceiving its surrounding environment and its relationship thereto. In some implementations, the means can be configured to access or otherwise obtain map data that is provided in a birds-eye view representation, such as generated by rasterization or other suitable processing format. A map data obtaining unit 710 is one example of a means for obtaining such map data as described herein.

The means can be configured to provide, as input to a machine-learned object detection model, the sensor data, and to receive the object detection data describing the features of the plurality of the actors relative to the autonomous vehicle as an output of the machine-learned object detection. The means can be configured to process the map data (e.g., geometric and/or semantic map priors) and the sensor data to generate data for inputting into the machine-learned object detection model. For example, ground height can be subtracted from the Z-value of each LiDAR point before voxelization. As a result, the variation caused by ground slope can be removed. Map data and/or semantic priors in the form of road and lane masks can be extracted. Each semantic prior can be or include a one-channel raster image in BEV depicting the drivable surface and all the lanes respectively. The means can augment the LiDAR voxel representation through concatenation with semantic map priors. The output space of the input representation can be defined in BEV, which can provide for efficient feature sharing between perception and prediction. Thus, the means can process sensor and map data to generate an input representation that can be input the object detection model. A machine learned object detection model unit 725 is one example of a means for processing the sensor data and map data to generate the input representation and/or inputting the input representation into the machine learned object detection model and receiving multiple outputs therefrom.

The means can be configured to generate motion forecast data that describes or predicts the trajectory/behavior of a plurality of actors with respect to the autonomous vehicle. The means can receive object detection data describing the features of the plurality of the actors relative to the autonomous vehicle from the machine learned object detection model(s). The means can receive relative location embedding data that describes a relative location of actors external to the autonomous vehicle. The means can input an input sequence that describes the object detection data and the relative location embedding data into an interaction model of an interaction transformer model and receive, as an output of the interaction model, an attention embedding. The means can input the attention embedding into a recurrent model of the interaction transformer model, and receive, as an output of the recurrent model, the motion forecast data that describes the respective trajectories for the plurality of actors. The trajectory/behavior forecasting unit(s) 715 is one example of a means for performing the above operations.

The means can be configured to determine a motion plan for the autonomous vehicle based at least in part on the motion forecast data. The means can be configured to determine a motion plan for the autonomous vehicle that best navigates the autonomous vehicle along a determined travel route relative to the objects at such locations. In some implementations, the means can be configured to determine a cost function for each of one or more candidate motion plans for the autonomous vehicle based at least in part on the current locations and/or predicted future locations and/or moving paths of the objects. In some implementations, the means can be configured to input the motion forecast data into a prediction model to receive respective trajectories for actors external to the autonomous vehicle. The respective trajectories can be used in part to determine the motion plan. A motion planning/control unit 735 is one example of a means for determining a motion plan for the autonomous vehicle.

The means can be configured to control one or more vehicle controls (e.g., actuators or other devices that control gas flow, steering, braking, etc.) to execute the selected motion plan. A vehicle controlling unit 720 is one example of a means for controlling motion of the autonomous vehicle to execute the motion plan.

While the present subject matter has been described in detail with respect to specific example embodiments and methods thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the scope of the present disclosure is by way of example rather than by way of limitation, and the subject disclosure does not preclude inclusion of such modifications, variations and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. 

1.-20. (canceled)
 21. A computer-implemented method, the method comprising: accessing, for a first iteration corresponding with a first time step, a first relative location embedding that describes respective relative locations of a plurality of actors with respect to an autonomous vehicle; processing the first relative location embedding by a machine-learned interaction transformer model to generate first motion forecast data describing movement of the plurality of actors at the first time step; generating, based on the first motion forecast data, a second relative location embedding for a second time step; processing the second relative location embedding by the machine-learned interaction transformer model to generate second motion forecast data for the plurality of actors at the second time step; and controlling motion of the autonomous vehicle based on at least one of the first motion forecast data or the second motion forecast data.
 22. The computer-implemented method of claim 21, wherein processing the first relative location embedding by a machine-learned interaction transformer model to generate first motion forecast data describing movement of the plurality of actors at the first time step further comprises: processing the first relative location embedding and a first input sequence describing object detection data corresponding with the first time step by the machine-learned interaction transformer model to generate the first motion forecast data describing movement of the plurality of actors at the first time step.
 23. The computer-implemented method of claim 21, wherein processing the second relative location embedding by a machine-learned interaction transformer model to generate second motion forecast data describing movement of the plurality of actors at the second time step further comprises: processing the second relative location embedding and a second input sequence describing object detection data corresponding with the second time step by the machine-learned interaction transformer model to generate the second motion forecast data describing movement of the plurality of actors at the second time step.
 24. The computer-implemented method of claim 23, wherein the second input sequence is based at least in part on the first motion forecast data.
 25. The computer-implemented method of claim 23, wherein the second input sequence further describes one or more features for the plurality of actors respectively at the first time step or the second time step, the one or more features comprising at least one of: a derivative of the respective relative location of a respective actor of the plurality of actors relative to the autonomous vehicle; a size of the respective actor; an orientation of the respective actor relative to the autonomous vehicle; or a center location of the respective actor.
 26. The computer-implemented method of claim 21, wherein one or more of the first relative location embedding and the second relative location embedding respectively, encodes the respective relative locations of the plurality of actors as a multi-channel positional embedding.
 27. The computer-implemented method of claim 21, further comprising: processing the second motion forecast data with a machine-learned prediction model to generate respective trajectories of the plurality of actors for the first time step and respective projected trajectories of the plurality of actors for the second time step; and controlling the motion of the autonomous vehicle further based on the respective trajectories and the respective projected trajectories generated by the machine-learned prediction model.
 28. The computer-implemented method of claim 27, wherein the machine-learned prediction model comprises one or more multi-layer perceptrons.
 29. The computer-implemented method of claim 27, further comprising: generating respective trajectory sequences for the plurality of actors, the respective trajectory sequences comprising the respective trajectories of the plurality of actors for the first time step and the respective projected trajectories of the plurality of actors for the second time step.
 30. The computer-implemented method of claim 21, wherein the machine-learned interaction transformer model comprises: a machine-learned interaction model configured to receive the first or second relative location embedding that describes the respective relative locations of the plurality of actors with respect to the autonomous vehicle, and in response to receipt of the relative location embedding, generate an attention embedding with respect to the plurality of actors; a machine-learned recurrent model configured to receive the attention embedding, and in response to receipt of the attention embedding, generate the first or second motion forecast data with respect to the plurality of actors.
 31. The computer-implemented method of claim 21, wherein the machine-learned interaction transformer model comprises one or more neural networks.
 32. A computing system for controlling motion of an autonomous vehicle, the computing system comprising: one or more processors; and one or more non-transitory computer-readable media that store instructions for execution by the one or more processors to cause the one or more processors to perform operations comprising: accessing, for a first iteration corresponding with a first time step, a first relative location embedding that describes respective relative locations of a plurality of actors with respect to the autonomous vehicle; processing the first relative location embedding by a machine-learned interaction transformer model to generate first motion forecast data describing movement of the plurality of actors at the first time step; generating, based on the first motion forecast data, a second relative location embedding for a second time step; processing the second relative location embedding by the machine-learned interaction transformer model to generate second motion forecast data for the plurality of actors at the second time step; and controlling motion of the autonomous vehicle based on at least one of the first motion forecast data or the second motion forecast data.
 33. The computing system for controlling autonomous vehicle motion of claim 32, wherein processing the first relative location embedding by a machine-learned interaction transformer model to generate first motion forecast data describing movement of the plurality of actors at the first time step further comprises: processing the first relative location embedding and a first input sequence describing object detection data corresponding with the first time step by the machine-learned interaction transformer model to generate the first motion forecast data describing movement of the plurality of actors at the first time step.
 34. The computing system for controlling autonomous vehicle motion of claim 32, wherein processing the second relative location embedding by a machine-learned interaction transformer model to generate second motion forecast data describing movement of the plurality of actors at the second time step further comprises: processing the second relative location embedding and a second input sequence describing object detection data corresponding with the second time step by the machine-learned interaction transformer model to generate the second motion forecast data describing movement of the plurality of actors at the second time step.
 35. The computing system for controlling autonomous vehicle motion of claim 34, wherein the second input sequence is based at least in part on the first motion forecast data.
 36. The computing system for controlling autonomous vehicle motion of claim 34, wherein the second input sequence further describes one or more features for the plurality of actors respectively at the first time step or the second time step, the one or more features comprising at least one of: a derivative of the respective relative location of a respective actor of the plurality of actors relative to the autonomous vehicle; a size of the respective actor; an orientation of the respective actor relative to the autonomous vehicle; or a center location of the respective actor.
 37. The computing system for controlling autonomous vehicle motion of claim 32, wherein one or more of the first relative location embedding and the second relative location embedding respectively encodes the respective relative locations of the plurality of actors as a multi-channel positional embedding.
 38. The computing system for controlling autonomous vehicle motion of claim 32, the operations further comprising: processing the second motion forecast data with a machine-learned prediction model to generate respective trajectories of the plurality of actors for the first time step and respective projected trajectories of the plurality of actors for the second time step; and controlling the motion of the autonomous vehicle further based on the respective trajectories and the respective projected trajectories generated by the machine-learned prediction model.
 39. An autonomous vehicle, comprising: one or more processors; and one or more non-transitory computer-readable media that store instructions for execution by the one or more processors to cause the one or more processors to perform operations comprising accessing, for a first iteration corresponding with a first time step, a first relative location embedding that describes respective relative locations of a plurality of actors with respect to the autonomous vehicle; processing the first relative location embedding by a machine-learned interaction transformer model to generate first motion forecast data describing movement of the plurality of actors at the first time step; generating, based on the first motion forecast data, a second relative location embedding for a second time step; processing the second relative location embedding by the machine-learned interaction transformer model to generate second motion forecast data for the plurality of actors at the second time step; and controlling motion of the autonomous vehicle based on at least one of the first motion forecast data or the second motion forecast data.
 40. The autonomous vehicle of claim 39, the operations further comprising: processing the second motion forecast data with a machine-learned prediction model to generate respective trajectories of the plurality of actors for the first time step and respective projected trajectories of the plurality of actors for the second time step; and controlling the motion of the autonomous vehicle further based on the respective trajectories and the respective projected trajectories generated by the machine-learned prediction model. 